Publications by Andreas Hotho

2017 [ nach oben ]

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  Ring, M., Wunderlich, S., Grüdl, D., Landes, D., Hotho, A.: A Toolset for Intrusion and Insider Threat Detection. In: Palomares Carrascosa, I., Kalutarage, H.K., and Huang, Y. (eds.) Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications. p. 3--31. Springer International Publishing, Cham (2017).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  Company data are a valuable asset and must be protected against unauthorized access and manipulation. In this contribution, we report on our ongoing work that aims to support IT security experts with identifying novel or obfuscated attacks in company networks, irrespective of their origin inside or outside the company network. A new toolset for anomaly based network intrusion detection is proposed. This toolset uses flow-based data which can be easily retrieved by central network components. We study the challenges of analysing flow-based data streams using data mining algorithms and build an appropriate approach step by step. In contrast to previous work, we collect flow-based data for each host over a certain time window, include the knowledge of domain experts and analyse the data from three different views. We argue that incorporating expert knowledge and previous flows allow us to create more meaningful attributes for subsequent analysis methods. This way, we try to detect novel attacks while simultaneously limiting the number of false positives.
  @inbook{Ring2017, abstract = {Company data are a valuable asset and must be protected against unauthorized access and manipulation. In this contribution, we report on our ongoing work that aims to support IT security experts with identifying novel or obfuscated attacks in company networks, irrespective of their origin inside or outside the company network. A new toolset for anomaly based network intrusion detection is proposed. This toolset uses flow-based data which can be easily retrieved by central network components. We study the challenges of analysing flow-based data streams using data mining algorithms and build an appropriate approach step by step. In contrast to previous work, we collect flow-based data for each host over a certain time window, include the knowledge of domain experts and analyse the data from three different views. We argue that incorporating expert knowledge and previous flows allow us to create more meaningful attributes for subsequent analysis methods. This way, we try to detect novel attacks while simultaneously limiting the number of false positives.}, address = {Cham}, author = {Ring, Markus and Wunderlich, Sarah and Grüdl, Dominik and Landes, Dieter and Hotho, Andreas}, booktitle = {Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications}, editor = {Palomares Carrascosa, Iván and Kalutarage, Harsha Kumara and Huang, Yan}, pages = {3--31}, publisher = {Springer International Publishing}, title = {A Toolset for Intrusion and Insider Threat Detection}, year = 2017 }
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  Becker, M., Lemmerich, F., Singer, P., Strohmaier, M., Hotho, A.: MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data. Data Mining and Knowledge Discovery. (2017).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  Sequential traces of user data are frequently observed online and offline, e.g., as sequences of visited websites or as sequences of locations captured by GPS. However, understanding factors explaining the production of sequence data is a challenging task, especially since the data generation is often not homogeneous. For example, navigation behavior might change in different phases of browsing a website or movement behavior may vary between groups of users. In this work, we tackle this task and propose MixedTrails , a Bayesian approach for comparing the plausibility of hypotheses regarding the generative processes of heterogeneous sequence data. Each hypothesis is derived from existing literature, theory, or intuition and represents a belief about transition probabilities between a set of states that can vary between groups of observed transitions. For example, when trying to understand human movement in a city and given some data, a hypothesis assuming tourists to be more likely to move towards points of interests than locals can be shown to be more plausible than a hypothesis assuming the opposite. Our approach incorporates such hypotheses as Bayesian priors in a generative mixed transition Markov chain model, and compares their plausibility utilizing Bayes factors. We discuss analytical and approximate inference methods for calculating the marginal likelihoods for Bayes factors, give guidance on interpreting the results, and illustrate our approach with several experiments on synthetic and empirical data from Wikipedia and Flickr. Thus, this work enables a novel kind of analysis for studying sequential data in many application areas.
  @article{Becker2017, abstract = {Sequential traces of user data are frequently observed online and offline, e.g., as sequences of visited websites or as sequences of locations captured by GPS. However, understanding factors explaining the production of sequence data is a challenging task, especially since the data generation is often not homogeneous. For example, navigation behavior might change in different phases of browsing a website or movement behavior may vary between groups of users. In this work, we tackle this task and propose MixedTrails , a Bayesian approach for comparing the plausibility of hypotheses regarding the generative processes of heterogeneous sequence data. Each hypothesis is derived from existing literature, theory, or intuition and represents a belief about transition probabilities between a set of states that can vary between groups of observed transitions. For example, when trying to understand human movement in a city and given some data, a hypothesis assuming tourists to be more likely to move towards points of interests than locals can be shown to be more plausible than a hypothesis assuming the opposite. Our approach incorporates such hypotheses as Bayesian priors in a generative mixed transition Markov chain model, and compares their plausibility utilizing Bayes factors. We discuss analytical and approximate inference methods for calculating the marginal likelihoods for Bayes factors, give guidance on interpreting the results, and illustrate our approach with several experiments on synthetic and empirical data from Wikipedia and Flickr. Thus, this work enables a novel kind of analysis for studying sequential data in many application areas.}, author = {Becker, Martin and Lemmerich, Florian and Singer, Philipp and Strohmaier, Markus and Hotho, Andreas}, journal = {Data Mining and Knowledge Discovery}, month = {jul}, title = {MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data}, year = 2017 }
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  Singer, P., Helic, D., Hotho, A., Strohmaier, M.: A Bayesian Method for Comparing Hypotheses About Human Trails. ACM Trans. Web. 11, 14:1--14:29 (2017).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful, for example, for improving underlying network structures, predicting user clicks, or enhancing recommendations. In this work, we present a method called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our method utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to calculate the evidence of the data under them. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method, and to compare the relative plausibility of hypotheses, we employ Bayes factors. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including Web site navigation, business reviews, and online music played. Our work expands the repertoire of methods available for studying human trails.
  @article{singer2017bayesian, abstract = {When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful, for example, for improving underlying network structures, predicting user clicks, or enhancing recommendations. In this work, we present a method called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our method utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to calculate the evidence of the data under them. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method, and to compare the relative plausibility of hypotheses, we employ Bayes factors. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including Web site navigation, business reviews, and online music played. Our work expands the repertoire of methods available for studying human trails.}, address = {New York, NY, USA}, author = {Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus}, journal = {ACM Trans. Web}, month = {jun}, number = 3, pages = {14:1--14:29}, publisher = {ACM}, title = {A Bayesian Method for Comparing Hypotheses About Human Trails}, volume = 11, year = 2017 }
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  Hotho, A., Jaeschke, R., Lerman, K.: Mining social semantics on the social web. Semantic Web. 8, 623--624 (2017).
  [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  @article{wrro115871, author = {Hotho, A. and Jaeschke, R. and Lerman, K.}, journal = {Semantic Web}, month = {April}, note = {copyright 2017 IOS Press and the authors. This is an author produced version of a paper subsequently published in Semantic Web. Uploaded in accordance with the publisher's self-archiving policy.}, number = 5, pages = {623--624}, publisher = {IOS Press}, title = {Mining social semantics on the social web}, volume = 8, year = 2017 }
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  Niebler, T., Becker, M., Pölitz, C., Hotho, A.: Learning Semantic Relatedness From Human Feedback Using Metric Learning, http://arxiv.org/abs/1705.07425, (2017).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  Assessing the degree of semantic relatedness between words is an important task with a variety of semantic applications, such as ontology learning for the Semantic Web, semantic search or query expansion. To accomplish this in an automated fashion, many relatedness measures have been proposed. However, most of these metrics only encode information contained in the underlying corpus and thus do not directly model human intuition. To solve this, we propose to utilize a metric learning approach to improve existing semantic relatedness measures by learning from additional information, such as explicit human feedback. For this, we argue to use word embeddings instead of traditional high-dimensional vector representations in order to leverage their semantic density and to reduce computational cost. We rigorously test our approach on several domains including tagging data as well as publicly available embeddings based on Wikipedia texts and navigation. Human feedback about semantic relatedness for learning and evaluation is extracted from publicly available datasets such as MEN or WS-353. We find that our method can significantly improve semantic relatedness measures by learning from additional information, such as explicit human feedback. For tagging data, we are the first to generate and study embeddings. Our results are of special interest for ontology and recommendation engineers, but also for any other researchers and practitioners of Semantic Web techniques.
  @misc{niebler2017learning, abstract = {Assessing the degree of semantic relatedness between words is an important task with a variety of semantic applications, such as ontology learning for the Semantic Web, semantic search or query expansion. To accomplish this in an automated fashion, many relatedness measures have been proposed. However, most of these metrics only encode information contained in the underlying corpus and thus do not directly model human intuition. To solve this, we propose to utilize a metric learning approach to improve existing semantic relatedness measures by learning from additional information, such as explicit human feedback. For this, we argue to use word embeddings instead of traditional high-dimensional vector representations in order to leverage their semantic density and to reduce computational cost. We rigorously test our approach on several domains including tagging data as well as publicly available embeddings based on Wikipedia texts and navigation. Human feedback about semantic relatedness for learning and evaluation is extracted from publicly available datasets such as MEN or WS-353. We find that our method can significantly improve semantic relatedness measures by learning from additional information, such as explicit human feedback. For tagging data, we are the first to generate and study embeddings. Our results are of special interest for ontology and recommendation engineers, but also for any other researchers and practitioners of Semantic Web techniques.}, author = {Niebler, Thomas and Becker, Martin and Pölitz, Christian and Hotho, Andreas}, note = {cite arxiv:1705.07425Comment: Under review at ISWC 2017}, title = {Learning Semantic Relatedness From Human Feedback Using Metric Learning}, year = 2017 }
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  Zoller, D., Doerfel, S., Pölitz, C., Hotho, A.: Leveraging User-Interactions for Time-Aware Tag Recommendations. Proceedings of the Workshop on Temporal Reasoning in Recommender Systems (2017).
  [ BibTeX ] [ Details ]
   
  @inproceedings{zoller2017leveraging, author = {Zoller, Daniel and Doerfel, Stephan and Pölitz, Christian and Hotho, Andreas}, booktitle = {Proceedings of the Workshop on Temporal Reasoning in Recommender Systems}, series = {CEUR Workshop Proceedings}, title = {Leveraging User-Interactions for Time-Aware Tag Recommendations}, year = 2017 }
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  Loreto, V., Haklay, M., Hotho, A., Servedio, V.C.P., Stumme, G., Theunis, J., Tria, F. eds: Participatory sensing, opinions and collective awareness. Springer (2017).
  [ BibTeX ] [ Details ]
   
  @book{loreto2017participatory, editor = {Loreto, Vittorio and Haklay, Mordechai and Hotho, Andreas and Servedio, Vito C. P. and Stumme, Gerd and Theunis, Jan and Tria, Francesca}, publisher = {Springer}, title = {Participatory sensing, opinions and collective awareness}, year = 2017 }

2016 [ nach oben ]

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  Cellier, P., Charnois, T., Hotho, A., Matwin, S., Moens, M.-F., Toussaint, Y. eds: Proceedings of the Workshop on Interactions between Data Mining and Natural Language Processing, DMNLP 2016, co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2016, Riva del Garda, Italy, September 23, 2016. CEUR-WS.org (2016).
  [ BibTeX ] [ URL ] [ Details ]
   
  @proceedings{conf/pkdd/2016dmnlp, booktitle = {DMNLP@PKDD/ECML}, editor = {Cellier, Peggy and Charnois, Thierry and Hotho, Andreas and Matwin, Stan and Moens, Marie-Francine and Toussaint, Yannick}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Proceedings of the Workshop on Interactions between Data Mining and Natural Language Processing, DMNLP 2016, co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2016, Riva del Garda, Italy, September 23, 2016.}, volume = 1646, year = 2016 }
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  Düking, P., Hotho, A., Fuss, F.K., Holmberg, H.-C., Sperlich, B.: Comparison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technologies. Frontiers in Physiology. 7, (2016).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  Athletes adapt their training daily to optimize performance, as well as avoid fatigue, overtraining and other undesirable effects on their health. To optimize training load, each athlete must take his/her own personal objective and subjective characteristics into consideration and an increasing number of wearable technologies (wearables) provide convenient monitoring of various parameters. Accordingly, it is important to help athletes decide which parameters are of primary interest and which wearables can monitor these parameters most effectively. Here, we discuss the wearable technologies available for non-invasive monitoring of various parameters concerning an athlete's training and health. On the basis of these considerations, we suggest directions for future development. Furthermore, we propose that a combination of several wearables is most effective for accessing all relevant parameters, disturbing the athlete as little as possible, and optimizing performance and promoting health.
  @article{10.3389/fphys.2016.00071, abstract = {Athletes adapt their training daily to optimize performance, as well as avoid fatigue, overtraining and other undesirable effects on their health. To optimize training load, each athlete must take his/her own personal objective and subjective characteristics into consideration and an increasing number of wearable technologies (wearables) provide convenient monitoring of various parameters. Accordingly, it is important to help athletes decide which parameters are of primary interest and which wearables can monitor these parameters most effectively. Here, we discuss the wearable technologies available for non-invasive monitoring of various parameters concerning an athlete's training and health. On the basis of these considerations, we suggest directions for future development. Furthermore, we propose that a combination of several wearables is most effective for accessing all relevant parameters, disturbing the athlete as little as possible, and optimizing performance and promoting health.}, author = {Düking, Peter and Hotho, Andreas and Fuss, Franz Konstantin and Holmberg, Hans-Christer and Sperlich, Billy}, journal = {Frontiers in Physiology}, number = 71, title = {Comparison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technologies}, volume = 7, year = 2016 }
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  Zoller, D., Doerfel, S., Jäschke, R., Stumme, G., Hotho, A.: Posted, Visited, Exported: Altmetrics in the Social Tagging System BibSonomy. Journal of Informetrics. 10, 732 - 749 (2016).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
   
  In social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from users’ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points.
  @article{zoller2016posted, abstract = {In social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from users’ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points.}, author = {Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas}, journal = {Journal of Informetrics}, number = 3, pages = {732 - 749}, title = {Posted, Visited, Exported: Altmetrics in the Social Tagging System BibSonomy}, volume = 10, year = 2016 }
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  Doerfel, S., Zoller, D., Singer, P., Niebler, T., Hotho, A., Strohmaier, M.: What Users Actually do in a Social Tagging System: A Study of User Behavior in BibSonomy. ACM Transactions on the Web. 10, 14:1--14:32 (2016).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  Social tagging systems have established themselves as an important part in today’s web and have attracted the interest of our research community in a variety of investigations. Henceforth, several aspects of social tagging systems have been discussed and assumptions have emerged on which our community builds their work. Yet, testing such assumptions has been difficult due to the absence of suitable usage data in the past. In this work, we thoroughly investigate and evaluate four aspects about tagging systems, covering social interaction, retrieval of posted resources, the importance of the three different types of entities, users, resources, and tags, as well as connections between these entities’ popularity in posted and in requested content. For that purpose, we examine live server log data gathered from the real-world, public social tagging system BibSonomy. Our empirical results paint a mixed picture about the four aspects. While for some, typical assumptions hold to a certain extent, other aspects need to be reflected in a very critical light. Our observations have implications for the understanding of social tagging systems, and the way they are used on the web. We make the dataset used in this work available to other researchers.
  @article{doerfel2016users, abstract = {Social tagging systems have established themselves as an important part in today’s web and have attracted the interest of our research community in a variety of investigations. Henceforth, several aspects of social tagging systems have been discussed and assumptions have emerged on which our community builds their work. Yet, testing such assumptions has been difficult due to the absence of suitable usage data in the past. In this work, we thoroughly investigate and evaluate four aspects about tagging systems, covering social interaction, retrieval of posted resources, the importance of the three different types of entities, users, resources, and tags, as well as connections between these entities’ popularity in posted and in requested content. For that purpose, we examine live server log data gathered from the real-world, public social tagging system BibSonomy. Our empirical results paint a mixed picture about the four aspects. While for some, typical assumptions hold to a certain extent, other aspects need to be reflected in a very critical light. Our observations have implications for the understanding of social tagging systems, and the way they are used on the web. We make the dataset used in this work available to other researchers.}, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, journal = {ACM Transactions on the Web}, month = {may}, number = 2, pages = {14:1--14:32}, publisher = {ACM}, title = {What Users Actually do in a Social Tagging System: A Study of User Behavior in BibSonomy}, volume = 10, year = 2016 }
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  Hettinger, L., Jannidis, F., Reger, I., Hotho, A.: Significance Testing for the Classification of Literary Subgenres. DH 2016 (2016).
  [ BibTeX ] [ Download ] [ Details ]
   
  @inproceedings{hettinger2016significance, author = {Hettinger, Lena and Jannidis, Fotis and Reger, Isabella and Hotho, Andreas}, booktitle = {DH 2016}, title = {Significance Testing for the Classification of Literary Subgenres}, year = 2016 }
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  Hettinger, L., Jannidis, F., Reger, I., Hotho, A.: Classification of Literary Subgenres. DHd 2016 (2016).
  [ BibTeX ] [ Download ] [ Details ]
   
  @inproceedings{hettinger2016classification, author = {Hettinger, Lena and Jannidis, Fotis and Reger, Isabella and Hotho, Andreas}, booktitle = {DHd 2016}, title = {Classification of Literary Subgenres}, year = 2016 }
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  Dallmann, A., Niebler, T., Lemmerich, F., Hotho, A.: Extracting Semantics from Random Walks on Wikipedia: Comparing learning and counting methods. In: West, R., Zia, L., Taraborelli, D., and Leskovec, J. (eds.) Wiki Workshop@ICWSM (2016).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  Semantic relatedness between words has been extracted from a variety of sources. In this ongoing work, we explore and compare several options for determining if semantic relatedness can be extracted from navigation structures in Wikipedia. In that direction, we first investigate the potential of representation learning techniques such as DeepWalk in comparison to previously applied methods based on counting co-occurrences. Since both methods are based on (random) paths in the network, we also study different approaches to generate paths from Wikipedia link structure. For this task, we do not only consider the link structure of Wikipedia, but also actual navigation behavior of users. Finally, we analyze if semantics can also be extracted from smaller subsets of the Wikipedia link network. As a result we find that representa- tion learning techniques mostly outperform the investigated co-occurrence counting methods on the Wikipedia network. However, we find that this is not the case for paths sampled from human navigation behavior.
  @inproceedings{dallmann2016extracting, abstract = {Semantic relatedness between words has been extracted from a variety of sources. In this ongoing work, we explore and compare several options for determining if semantic relatedness can be extracted from navigation structures in Wikipedia. In that direction, we first investigate the potential of representation learning techniques such as DeepWalk in comparison to previously applied methods based on counting co-occurrences. Since both methods are based on (random) paths in the network, we also study different approaches to generate paths from Wikipedia link structure. For this task, we do not only consider the link structure of Wikipedia, but also actual navigation behavior of users. Finally, we analyze if semantics can also be extracted from smaller subsets of the Wikipedia link network. As a result we find that representa- tion learning techniques mostly outperform the investigated co-occurrence counting methods on the Wikipedia network. However, we find that this is not the case for paths sampled from human navigation behavior.}, author = {Dallmann, Alexander and Niebler, Thomas and Lemmerich, Florian and Hotho, Andreas}, booktitle = {Wiki Workshop@ICWSM}, editor = {West, Robert and Zia, Leila and Taraborelli, Dario and Leskovec, Jure}, title = {Extracting Semantics from Random Walks on Wikipedia: Comparing learning and counting methods}, year = 2016 }
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  Lemmerich, F., Becker, M., Singer, P., Helic, D., Hotho, A., Strohmaier, M.: Mining Subgroups with Exceptional Transition Behavior. In: Krishnapuram, B., Shah, M., Smola, A.J., Aggarwal, C., Shen, D., and Rastogi, R. (eds.) KDD. pp. 965-974. ACM (2016).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{lemmerich2016mining, author = {Lemmerich, Florian and Becker, Martin and Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus}, booktitle = {KDD}, crossref = {conf/kdd/2016}, editor = {Krishnapuram, Balaji and Shah, Mohak and Smola, Alexander J. and Aggarwal, Charu and Shen, Dou and Rastogi, Rajeev}, pages = {965-974}, publisher = {ACM}, title = {Mining Subgroups with Exceptional Transition Behavior}, year = 2016 }
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  Zehe, A., Becker, M., Hettinger, L., Hotho, A., Reger, I., Jannidis, F.: Prediction of Happy Endings in German Novels. In: Cellier, P., Charnois, T., Hotho, A., Matwin, S., Moens, M.-F., and Toussaint, Y. (eds.) DMNLP@PKDD/ECML. pp. 9-16. CEUR-WS.org (2016).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{zehe2016prediction, author = {Zehe, Albin and Becker, Martin and Hettinger, Lena and Hotho, Andreas and Reger, Isabella and Jannidis, Fotis}, booktitle = {DMNLP@PKDD/ECML}, crossref = {conf/pkdd/2016dmnlp}, editor = {Cellier, Peggy and Charnois, Thierry and Hotho, Andreas and Matwin, Stan and Moens, Marie-Francine and Toussaint, Yannick}, pages = {9-16}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Prediction of Happy Endings in German Novels}, volume = 1646, year = 2016 }
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  Niebler, T., Schlör, D., Becker, M., Hotho, A.: Extracting Semantics from Unconstrained Navigation on Wikipedia. KI. 30, 163-168 (2016).
  [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  @article{journals/ki/NieblerS0H16, author = {Niebler, Thomas and Schlör, Daniel and Becker, Martin and Hotho, Andreas}, journal = {KI}, number = 2, pages = {163-168}, title = {Extracting Semantics from Unconstrained Navigation on Wikipedia}, volume = 30, year = 2016 }
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  Becker, M., Mewes, H., Hotho, A., Dimitrov, D., Lemmerich, F., Strohmaier, M.: SparkTrails: A MapReduce Implementation of HypTrails for Comparing Hypotheses About Human Trails. In: Bourdeau, J., Hendler, J., Nkambou, R., Horrocks, I., and Zhao, B.Y. (eds.) WWW (Companion Volume). pp. 17-18. ACM (2016).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{conf/www/0003MHDLS16, author = {Becker, Martin and Mewes, Hauke and Hotho, Andreas and Dimitrov, Dimitar and Lemmerich, Florian and Strohmaier, Markus}, booktitle = {WWW (Companion Volume)}, crossref = {conf/www/2016c}, editor = {Bourdeau, Jacqueline and Hendler, Jim and Nkambou, Roger and Horrocks, Ian and Zhao, Ben Y.}, pages = {17-18}, publisher = {ACM}, title = {SparkTrails: A MapReduce Implementation of HypTrails for Comparing Hypotheses About Human Trails.}, year = 2016 }
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  Jannidis, F., Reger, I., Zehe, A., Becker, M., Hettinger, L., Hotho, A.: Analyzing Features for the Detection of Happy Endings in German Novels, http://arxiv.org/abs/1611.09028, (2016).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  With regard to a computational representation of literary plot, this paper looks at the use of sentiment analysis for happy ending detection in German novels. Its focus lies on the investigation of previously proposed sentiment features in order to gain insight about the relevance of specific features on the one hand and the implications of their performance on the other hand. Therefore, we study various partitionings of novels, considering the highly variable concept of "ending". We also show that our approach, even though still rather simple, can potentially lead to substantial findings relevant to literary studies.
  @misc{jannidis2016analyzing, abstract = {With regard to a computational representation of literary plot, this paper looks at the use of sentiment analysis for happy ending detection in German novels. Its focus lies on the investigation of previously proposed sentiment features in order to gain insight about the relevance of specific features on the one hand and the implications of their performance on the other hand. Therefore, we study various partitionings of novels, considering the highly variable concept of "ending". We also show that our approach, even though still rather simple, can potentially lead to substantial findings relevant to literary studies.}, author = {Jannidis, Fotis and Reger, Isabella and Zehe, Albin and Becker, Martin and Hettinger, Lena and Hotho, Andreas}, note = {cite arxiv:1611.09028}, title = {Analyzing Features for the Detection of Happy Endings in German Novels}, year = 2016 }
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  Niebler, T., Becker, M., Zoller, D., Doerfel, S., Hotho, A.: FolkTrails: Interpreting Navigation Behavior in a Social Tagging System. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, New York, NY, USA (2016).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  Social tagging systems have established themselves as a quick and easy way to organize information by annotating resources with tags. In recent work, user behavior in social tagging systems was studied, that is, how users assign tags, and consume content. However, it is still unclear how users make use of the navigation options they are given. Understanding their behavior and differences in behavior of different user groups is an important step towards assessing the effectiveness of a navigational concept and of improving it to better suit the users’ needs. In this work, we investigate navigation trails in the popular scholarly social tagging system BibSonomy from six years of log data. We discuss dynamic browsing behavior of the general user population and show that different navigational subgroups exhibit different navigational traits. Furthermore, we provide strong evidence that the semantic nature of the underlying folksonomy is an essential factor for explaining navigation.
  @inproceedings{niebler2016folktrails, abstract = {Social tagging systems have established themselves as a quick and easy way to organize information by annotating resources with tags. In recent work, user behavior in social tagging systems was studied, that is, how users assign tags, and consume content. However, it is still unclear how users make use of the navigation options they are given. Understanding their behavior and differences in behavior of different user groups is an important step towards assessing the effectiveness of a navigational concept and of improving it to better suit the users’ needs. In this work, we investigate navigation trails in the popular scholarly social tagging system BibSonomy from six years of log data. We discuss dynamic browsing behavior of the general user population and show that different navigational subgroups exhibit different navigational traits. Furthermore, we provide strong evidence that the semantic nature of the underlying folksonomy is an essential factor for explaining navigation.}, address = {New York, NY, USA}, author = {Niebler, Thomas and Becker, Martin and Zoller, Daniel and Doerfel, Stephan and Hotho, Andreas}, booktitle = {Proceedings of the 25th ACM International on Conference on Information and Knowledge Management}, publisher = {ACM}, series = {CIKM '16}, title = {FolkTrails: Interpreting Navigation Behavior in a Social Tagging System}, year = 2016 }
• 
  Otto, F., Ring, M., Landes, D., Hotho, A.: Creation of Specific Flow-Based Training Data Sets for Usage Behaviour Classification. ECCWS2016-Proceedings fo the 15th European Conference on Cyber Warfare and Security. p. 437. Academic Conferences and publishing limited (2016).
  [ BibTeX ] [ Details ]
   
  @inproceedings{otto2016creation, author = {Otto, Florian and Ring, Markus and Landes, Dieter and Hotho, Andreas}, booktitle = {ECCWS2016-Proceedings fo the 15th European Conference on Cyber Warfare and Security}, organization = {Academic Conferences and publishing limited}, pages = 437, title = {Creation of Specific Flow-Based Training Data Sets for Usage Behaviour Classification}, year = 2016 }

2015 [ nach oben ]

• 
  Singer, P., Helic, D., Hotho, A., Strohmaier, M.: Hyptrails: A bayesian approach for comparing hypotheses about human trails. 24th International World Wide Web Conference (WWW2015). ACM, Firenze, Italy (2015).
  [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  @inproceedings{singer2015hyptrails, address = {Firenze, Italy}, author = {Singer, P. and Helic, D. and Hotho, A. and Strohmaier, M.}, booktitle = {24th International World Wide Web Conference (WWW2015)}, month = {May 18 - May 22}, organization = {ACM}, publisher = {ACM}, title = {Hyptrails: A bayesian approach for comparing hypotheses about human trails}, year = 2015 }
• 
  v. Kistowski, J., Nikolas, H., Zoller, D., Kounev, S., Hotho, A.: Modeling and Extracting Load Intensity Profiles. Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) (2015).
  [ Abstract ] [ BibTeX ] [ Details ]
   
  Today’s system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Benchmarking of systems under these constraints is difficult, as state-of-the-art benchmarking frameworks provide only limited support for emulating such dynamic and highly vari- able load profiles for the creation of realistic workload scenarios. Industrial benchmarks typically confine themselves to workloads with constant or stepwise increasing loads. Alternatively, they support replaying of recorded load traces. Statistical load inten- sity descriptions also do not sufficiently capture concrete pattern load profile variations over time. To address these issues, we present the Descartes Load Intensity Model (DLIM). DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance can be used as a compact representation of a recorded load intensity trace, providing a powerful tool for benchmarking and performance analysis. As manually obtaining DLIM instances can be time consuming, we present three different automated extraction methods, which also help to enable autonomous system analysis for self-adaptive systems. Model expressiveness is validated using the presented extraction methods. Extracted DLIM instances exhibit a median modeling error of 12.4% on average over nine different real-world traces covering between two weeks and seven months. Additionally, extraction methods perform orders of magnitude faster than existing time series decomposition approaches.
  @inproceedings{vkistowski2015modeling, abstract = {Today’s system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Benchmarking of systems under these constraints is difficult, as state-of-the-art benchmarking frameworks provide only limited support for emulating such dynamic and highly vari- able load profiles for the creation of realistic workload scenarios. Industrial benchmarks typically confine themselves to workloads with constant or stepwise increasing loads. Alternatively, they support replaying of recorded load traces. Statistical load inten- sity descriptions also do not sufficiently capture concrete pattern load profile variations over time. To address these issues, we present the Descartes Load Intensity Model (DLIM). DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance can be used as a compact representation of a recorded load intensity trace, providing a powerful tool for benchmarking and performance analysis. As manually obtaining DLIM instances can be time consuming, we present three different automated extraction methods, which also help to enable autonomous system analysis for self-adaptive systems. Model expressiveness is validated using the presented extraction methods. Extracted DLIM instances exhibit a median modeling error of 12.4% on average over nine different real-world traces covering between two weeks and seven months. Additionally, extraction methods perform orders of magnitude faster than existing time series decomposition approaches.}, author = {v. Kistowski, Jóakim and Nikolas, Herbst. and Zoller, Daniel and Kounev, Samuel and Hotho, Andreas}, booktitle = {Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)}, title = {Modeling and Extracting Load Intensity Profiles}, year = 2015 }
• 
  Dallmann, A., Lemmerich, F., Zoller, D., Hotho, A.: Media Bias in German Online Newspapers. 26th ACM Conference on Hypertext and Social Media. ACM, Cyprus, Turkey, September 1-4 (2015).
  [ BibTeX ] [ Download ] [ Details ]
   
  @inproceedings{dallmann2015media, address = {Cyprus, Turkey, September 1-4}, author = {Dallmann, Alexander and Lemmerich, Florian and Zoller, Daniel and Hotho, Andreas}, booktitle = {26th ACM Conference on Hypertext and Social Media}, publisher = {ACM}, title = {Media Bias in German Online Newspapers}, year = 2015 }
• 
  Ring, M., Otto, F., Becker, M., Niebler, T., Landes, D., Hotho, A.: ConDist: A Context-Driven Categorical Distance Measure. Presented at the (2015).
  [ BibTeX ] [ Download ] [ Details ]
   
  @inproceedings{ring2015condist, author = {Ring, Markus and Otto, Florian and Becker, Martin and Niebler, Thomas and Landes, Dieter and Hotho, Andreas}, editor = {ECMLPKDD2015}, title = {ConDist: A Context-Driven Categorical Distance Measure}, year = 2015 }
• 
  Zoller, D., Doerfel, S., Jäschke, R., Stumme, G., Hotho, A.: On Publication Usage in a Social Bookmarking System. Proceedings of the 2015 ACM Conference on Web Science (2015).
  [ Abstract ] [ BibTeX ] [ Details ]
   
  Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.
  @inproceedings{zoller2015publication, abstract = {Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.}, author = {Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas}, booktitle = {Proceedings of the 2015 ACM Conference on Web Science}, title = {On Publication Usage in a Social Bookmarking System}, year = 2015 }
• 
  Becker, M., Borchert, K., Hirth, M., Mewes, H., Hotho, A., Tran-Gia, P.: MicroTrails: Comparing Hypotheses About Task Selection on a Crowdsourcing Platform. Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business. p. 10:1--10:8. ACM, Graz, Austria (2015).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  To optimize the workflow on commercial crowdsourcing platforms like Amazon Mechanical Turk or Microworkers, it is important to understand how users choose their tasks. Current work usually explores the underlying processes by employing user studies based on surveys with a limited set of participants. In contrast, we formulate hypotheses based on the different findings in these studies and, instead of verifying them based on user feedback, we compare them directly on data from a commercial crowdsourcing platform. For evaluation, we use a Bayesian approach called HypTrails which allows us to give a relative ranking of the corresponding hypotheses. The hypotheses considered, are for example based on task categories, monetary incentives or semantic similarity of task descriptions. We find that, in our scenario, hypotheses based on employers as well the the task descriptions work best. Overall, we objectively compare different factors influencing users when choosing their tasks. Our approach enables crowdsourcing companies to better understand their users in order to optimize their platforms, e.g., by incorparting the gained knowledge about these factors into task recommentation systems.
  @inproceedings{becker2015microtrails, abstract = {To optimize the workflow on commercial crowdsourcing platforms like Amazon Mechanical Turk or Microworkers, it is important to understand how users choose their tasks. Current work usually explores the underlying processes by employing user studies based on surveys with a limited set of participants. In contrast, we formulate hypotheses based on the different findings in these studies and, instead of verifying them based on user feedback, we compare them directly on data from a commercial crowdsourcing platform. For evaluation, we use a Bayesian approach called HypTrails which allows us to give a relative ranking of the corresponding hypotheses. The hypotheses considered, are for example based on task categories, monetary incentives or semantic similarity of task descriptions. We find that, in our scenario, hypotheses based on employers as well the the task descriptions work best. Overall, we objectively compare different factors influencing users when choosing their tasks. Our approach enables crowdsourcing companies to better understand their users in order to optimize their platforms, e.g., by incorparting the gained knowledge about these factors into task recommentation systems.}, address = {New York, NY, USA}, author = {Becker, Martin and Borchert, Kathrin and Hirth, Matthias and Mewes, Hauke and Hotho, Andreas and Tran-Gia, Phuoc}, booktitle = {Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business}, pages = {10:1--10:8}, publisher = {ACM}, series = {i-KNOW '15}, title = {MicroTrails: Comparing Hypotheses About Task Selection on a Crowdsourcing Platform}, year = 2015 }
• 
  Sîrbu, A., Becker, M., Caminiti, S., De Baets, B., Elen, B., Francis, L., Gravino, P., Hotho, A., Ingarra, S., Loreto, V., Molino, A., Mueller, J., Peters, J., Ricchiuti, F., Saracino, F., Servedio, V.D.P., Stumme, G., Theunis, J., Tria, F., Van den Bossche, J.: Participatory Patterns in an International Air Quality Monitoring Initiative. PLoS ONE. 10, e0136763 (2015).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
   
  <p>The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.</p>
  @article{10.1371/journal.pone.0136763, abstract = {<p>The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.</p>}, author = {Sîrbu, Alina and Becker, Martin and Caminiti, Saverio and De Baets, Bernard and Elen, Bart and Francis, Louise and Gravino, Pietro and Hotho, Andreas and Ingarra, Stefano and Loreto, Vittorio and Molino, Andrea and Mueller, Juergen and Peters, Jan and Ricchiuti, Ferdinando and Saracino, Fabio and Servedio, Vito D. P. and Stumme, Gerd and Theunis, Jan and Tria, Francesca and Van den Bossche, Joris}, journal = {PLoS ONE}, month = {08}, number = 8, pages = {e0136763}, publisher = {Public Library of Science}, title = {Participatory Patterns in an International Air Quality Monitoring Initiative}, volume = 10, year = 2015 }
• 
  Sîrbu, A., Becker, M., Caminiti, S., De Baets, B., Elen, B., Francis, L., Gravino, P., Hotho, A., Ingarra, S., Loreto, V., Molino, A., Mueller, J., Peters, J., Ricchiuti, F., Saracino, F., Servedio, V.D.P., Stumme, G., Theunis, J., Tria, F., Van den Bossche, J.: Participatory Patterns in an International Air Quality Monitoring Initiative, http://arxiv.org/abs/1503.07730, (2015).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.
  @misc{sirbu2015participatory, abstract = {The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.}, author = {Sîrbu, Alina and Becker, Martin and Caminiti, Saverio and De Baets, Bernard and Elen, Bart and Francis, Louise and Gravino, Pietro and Hotho, Andreas and Ingarra, Stefano and Loreto, Vittorio and Molino, Andrea and Mueller, Juergen and Peters, Jan and Ricchiuti, Ferdinando and Saracino, Fabio and Servedio, Vito D. P. and Stumme, Gerd and Theunis, Jan and Tria, Francesca and Van den Bossche, Joris}, note = {cite arxiv:1503.07730Comment: 17 pages, 6 figures, 1 supplementary file}, title = {Participatory Patterns in an International Air Quality Monitoring Initiative}, year = 2015 }
• 
  Becker, M., Singer, P., Lemmerich, F., Hotho, A., Helic, D., Strohmaier, M.: VizTrails: An Information Visualization Tool for Exploring Geographic Movement Trajectories. Proceedings of the 26th ACM Conference on Hypertext & Social Media. p. 319--320. ACM, Guzelyurt, Northern Cyprus (2015).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
   
  Understanding the way people move through urban areas represents an important problem that has implications for a range of societal challenges such as city planning, public transportation, or crime analysis. In this paper, we present an interactive visualization tool called VizTrails for exploring and understanding such human movement. It features visualizations that show aggregated statistics of trails for geographic areas that correspond to grid cells on a map, e.g., on the number of users passing through or on cells commonly visited next. Amongst other features, system allows to overlay the map with the results of SPARQL queries in order to relate the observed trajectory statistics with its geo-spatial context, e.g., considering a city's points of interest. The systems functionality is demonstrated using trajectory examples extracted from the social photo sharing platform Flickr. Overall, VizTrails facilitates deeper insights into geo-spatial trajectory data by enabling interactive exploration of aggregated statistics and providing geo-spatial context.
  @inproceedings{Becker:2015:VIV:2700171.2791021, abstract = {Understanding the way people move through urban areas represents an important problem that has implications for a range of societal challenges such as city planning, public transportation, or crime analysis. In this paper, we present an interactive visualization tool called VizTrails for exploring and understanding such human movement. It features visualizations that show aggregated statistics of trails for geographic areas that correspond to grid cells on a map, e.g., on the number of users passing through or on cells commonly visited next. Amongst other features, system allows to overlay the map with the results of SPARQL queries in order to relate the observed trajectory statistics with its geo-spatial context, e.g., considering a city's points of interest. The systems functionality is demonstrated using trajectory examples extracted from the social photo sharing platform Flickr. Overall, VizTrails facilitates deeper insights into geo-spatial trajectory data by enabling interactive exploration of aggregated statistics and providing geo-spatial context.}, address = {New York, NY, USA}, author = {Becker, Martin and Singer, Philipp and Lemmerich, Florian and Hotho, Andreas and Helic, Denis and Strohmaier, Markus}, booktitle = {Proceedings of the 26th ACM Conference on Hypertext & Social Media}, pages = {319--320}, publisher = {ACM}, series = {HT '15}, title = {VizTrails: An Information Visualization Tool for Exploring Geographic Movement Trajectories}, year = 2015 }
• 
  Hettinger, L., Becker, M., Reger, I., Jannidis, F., Hotho, A.: Genre classification on German novels. Proceedings of the 12th International Workshop on Text-based Information Retrieval (2015).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{schwemmlein2015genre, author = {Hettinger, Lena and Becker, Martin and Reger, Isabella and Jannidis, Fotis and Hotho, Andreas}, booktitle = {Proceedings of the 12th International Workshop on Text-based Information Retrieval}, title = {Genre classification on German novels}, year = 2015 }
• 
  Becker, M., Singer, P., Lemmerich, F., Hotho, A., Helic, D., Strohmaier, M.: Photowalking the city: comparing hypotheses about urban photo trails on Flickr. Proceedings of the 7th International Conference on SocialInformatics (to appear). , Beijing, China (2015).
  [ BibTeX ] [ Details ]
   
  @inproceedings{becker2015photowalking, address = {Beijing, China}, author = {Becker, Martin and Singer, Philipp and Lemmerich, Florian and Hotho, Andreas and Helic, Denis and Strohmaier, Markus}, booktitle = {Proceedings of the 7th International Conference on SocialInformatics (to appear)}, month = {dec}, title = {Photowalking the city: comparing hypotheses about urban photo trails on Flickr}, type = {Publication}, year = 2015 }

2014 [ nach oben ]

• 
  Jannach, D., Freyne, J., Geyer, W., Guy, I., Hotho, A., Mobasher, B. eds: Proceedings of the 6th Workshop on Recommender Systems and the Social Web (RSWeb 2014) co-located with the 8th ACM Conference on Recommender Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014. CEUR-WS.org (2014).
  [ BibTeX ] [ URL ] [ Details ]
   
  @proceedings{jannach2014proceedings, editor = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Proceedings of the 6th Workshop on Recommender Systems and the Social Web (RSWeb 2014) co-located with the 8th ACM Conference on Recommender Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014}, volume = 1271, year = 2014 }
• 
  Cellier, P., Charnois, T., Hotho, A., Matwin, S., Moens, M.-F., Toussaint, Y. eds: Proceedings of the 1st International Workshop on Interactions between Data Mining and Natural Language Processing co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, DMNLP@PKDD/ECML 2014, Nancy, France, September 15, 2014. CEUR-WS.org (2014).
  [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  @proceedings{cellier2014proceedings, editor = {Cellier, Peggy and Charnois, Thierry and Hotho, Andreas and Matwin, Stan and Moens, Marie-Francine and Toussaint, Yannick}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Proceedings of the 1st International Workshop on Interactions between Data Mining and Natural Language Processing co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, DMNLP@PKDD/ECML 2014, Nancy, France, September 15, 2014}, volume = 1202, year = 2014 }
• 
  Atzmueller, M., Becker, M., Kibanov, M., Scholz, C., Doerfel, S., Hotho, A., Macek, B.-E., Mitzlaff, F., Mueller, J., Stumme, G.: Ubicon and its Applications for Ubiquitous Social Computing. New Review of Hypermedia and Multimedia. 20, 53--77 (2014).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.
  @article{atzmueller2014ubicon, abstract = {The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.}, author = {Atzmueller, Martin and Becker, Martin and Kibanov, Mark and Scholz, Christoph and Doerfel, Stephan and Hotho, Andreas and Macek, Bjoern-Elmar and Mitzlaff, Folke and Mueller, Juergen and Stumme, Gerd}, journal = {New Review of Hypermedia and Multimedia}, number = 1, pages = {53--77}, title = {Ubicon and its Applications for Ubiquitous Social Computing}, volume = 20, year = 2014 }
• 
  Doerfel, S., Zoller, D., Singer, P., Niebler, T., Hotho, A., Strohmaier, M.: Evaluating Assumptions about Social Tagging - A Study of User Behavior in BibSonomy. In: Seidl, T., Hassani, M., and Beecks, C. (eds.) Proceedings of the 16th LWA Workshops: KDML, IR and FGWM, Aachen, Germany, September 8-10, 2014. p. 18--19. CEUR-WS.org (2014).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{doerfel2014evaluating, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Proceedings of the 16th LWA Workshops: KDML, IR and FGWM, Aachen, Germany, September 8-10, 2014.}, editor = {Seidl, Thomas and Hassani, Marwan and Beecks, Christian}, pages = {18--19}, publisher = {CEUR-WS.org}, title = {Evaluating Assumptions about Social Tagging - A Study of User Behavior in BibSonomy}, year = 2014 }
• 
  Doerfel, S., Zoller, D., Singer, P., Niebler, T., Hotho, A., Strohmaier, M.: How Social is Social Tagging? Proceedings of the 23rd International World Wide Web Conference. ACM, New York, NY, USA (2014).
  [ BibTeX ] [ Download ] [ Details ]
   
  @inproceedings{doerfel2014social, address = {New York, NY, USA}, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Proceedings of the 23rd International World Wide Web Conference}, publisher = {ACM}, series = {WWW 2014}, title = {How Social is Social Tagging?}, year = 2014 }
• 
  Jannach, D., Freyne, J., Geyer, W., Guy, I., Hotho, A., Mobasher, B.: The sixth ACM RecSys workshop on recommender systems and the social web. Eighth ACM Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, USA - October 06 - 10, 2014. p. 395 (2014).
  [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  @inproceedings{jannach2014sixth, author = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, booktitle = {Eighth ACM Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, USA - October 06 - 10, 2014}, pages = 395, title = {The sixth ACM RecSys workshop on recommender systems and the social web}, year = 2014 }
• 
  Singer, P., Helic, D., Hotho, A., Strohmaier, M.: HypTrails: A Bayesian Approach for Comparing Hypotheses about Human Trails on the Web, http://arxiv.org/abs/1411.2844, (2014).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.
  @misc{singer2014hyptrails, abstract = {When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.}, author = {Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus}, note = {cite arxiv:1411.2844}, title = {HypTrails: A Bayesian Approach for Comparing Hypotheses about Human Trails on the Web}, year = 2014 }
• 
  Becker, M., Hotho, A., Mueller, J., Kibanov, M., Atzmueller, M., Stumme, G.: Subjective vs. Objective Data: Bridging the Gap, http://www.gesis.org/en/events/css-wintersymposium/poster-presentation/, (2014).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  Sensor data is objective. But when measuring our environment, measured values are contrasted with our perception, which is always subjective. This makes interpreting sensor measurements difficult for a single person in her personal environment. In this context, the EveryAware projects directly connects the concepts of objective sensor data with subjective impressions and perceptions by providing a collective sensing platform with several client applications allowing to explicitly associate those two data types. The goal is to provide the user with personalized feedback, a characterization of the global as well as her personal environment, and enable her to position her perceptions in this global context. In this poster we summarize the collected data of two EveryAware applications, namely WideNoise for noise measurements and AirProbe for participatory air quality sensing. Basic insights are presented including user activity, learning processes and sensor data to perception correlations. These results provide an outlook on how this data can further be used to understand the connection between sensor data and perceptions.
  @misc{becker2014subjective, abstract = {Sensor data is objective. But when measuring our environment, measured values are contrasted with our perception, which is always subjective. This makes interpreting sensor measurements difficult for a single person in her personal environment. In this context, the EveryAware projects directly connects the concepts of objective sensor data with subjective impressions and perceptions by providing a collective sensing platform with several client applications allowing to explicitly associate those two data types. The goal is to provide the user with personalized feedback, a characterization of the global as well as her personal environment, and enable her to position her perceptions in this global context. In this poster we summarize the collected data of two EveryAware applications, namely WideNoise for noise measurements and AirProbe for participatory air quality sensing. Basic insights are presented including user activity, learning processes and sensor data to perception correlations. These results provide an outlook on how this data can further be used to understand the connection between sensor data and perceptions.}, author = {Becker, Martin and Hotho, Andreas and Mueller, Juergen and Kibanov, Mark and Atzmueller, Martin and Stumme, Gerd}, howpublished = {CSSWS 2014, Poster}, title = {Subjective vs. Objective Data: Bridging the Gap}, year = 2014 }
• 
  Doerfel, S., Zoller, D., Singer, P., Niebler, T., Hotho, A., Strohmaier, M.: Of course we share! Testing Assumptions about Social Tagging Systems, http://arxiv.org/abs/1401.0629, (2014).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  Social tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior.
  @misc{doerfel2014course, abstract = {Social tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior.}, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, note = {cite arxiv:1401.0629}, title = {Of course we share! Testing Assumptions about Social Tagging Systems}, year = 2014 }
• 
  Mitzlaff, F., Atzmueller, M., Hotho, A., Stumme, G.: The social distributional hypothesis: a pragmatic proxy for homophily in online social networks. Social Network Analysis and Mining. 4, (2014).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
   
  Applications of the Social Web are ubiquitous and have become an integral part of everyday life: Users make friends, for example, with the help of online social networks, share thoughts via Twitter, or collaboratively write articles in Wikipedia. All such interactions leave digital traces; thus, users participate in the creation of heterogeneous, distributed, collaborative data collections. In linguistics, the
  @article{noKey, abstract = {Applications of the Social Web are ubiquitous and have become an integral part of everyday life: Users make friends, for example, with the help of online social networks, share thoughts via Twitter, or collaboratively write articles in Wikipedia. All such interactions leave digital traces; thus, users participate in the creation of heterogeneous, distributed, collaborative data collections. In linguistics, the}, author = {Mitzlaff, Folke and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd}, journal = {Social Network Analysis and Mining}, number = 1, publisher = {Springer Vienna}, title = {The social distributional hypothesis: a pragmatic proxy for homophily in online social networks}, volume = 4, year = 2014 }
• 
  Singer, P., Niebler, T., Hotho, A., Strohmaier, M.: Folksonomies. Encyclopedia of Social Network Analysis and Mining. p. 542--547. Springer (2014).
  [ BibTeX ] [ Details ]
   
  @incollection{singer2014folksonomies, author = {Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Encyclopedia of Social Network Analysis and Mining}, pages = {542--547}, publisher = {Springer}, title = {Folksonomies}, year = 2014 }

2013 [ nach oben ]

• 
  Landia, N., Doerfel, S., Jäschke, R., Anand, S.S., Hotho, A., Griffiths, N.: Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations. cs.IR. 1310.1498, (2013).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.
  @article{landia2013deeper, abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.}, author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan}, journal = {cs.IR}, title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations}, volume = {1310.1498}, year = 2013 }
• 
  Mitzlaff, F., Atzmueller, M., Stumme, G., Hotho, A.: Semantics of User Interaction in Social Media. In: Ghoshal, G., Poncela-Casasnovas, J., and Tolksdorf, R. (eds.) Complex Networks IV. Springer Verlag, Heidelberg, Germany (2013).
  [ BibTeX ] [ DOI ] [ Details ]
   
  @incollection{MASH:13, address = {Heidelberg, Germany}, author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas}, booktitle = {Complex Networks IV}, editor = {Ghoshal, Gourab and Poncela-Casasnovas, Julia and Tolksdorf, Robert}, publisher = {Springer Verlag}, series = {Studies in Computational Intelligence}, title = {Semantics of User Interaction in Social Media}, volume = 476, year = 2013 }
• 
  Niebler, T., Singer, P., Benz, D., Körner, C., Strohmaier, M., Hotho, A.: How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., and Yilmaz, E. (eds.) Advances in Information Retrieval. pp. 86-97. Springer Berlin Heidelberg (2013).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which
  @incollection{niebler2013tagging, abstract = {The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which}, author = {Niebler, Thomas and Singer, Philipp and Benz, Dominik and Körner, Christian and Strohmaier, Markus and Hotho, Andreas}, booktitle = {Advances in Information Retrieval}, editor = {Serdyukov, Pavel and Braslavski, Pavel and Kuznetsov, SergeiO. and Kamps, Jaap and Rüger, Stefan and Agichtein, Eugene and Segalovich, Ilya and Yilmaz, Emine}, pages = {86-97}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, title = {How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems}, volume = 7814, year = 2013 }
• 
  Becker, M., Mueller, J., Hotho, A., Stumme, G.: A Generic Platform for Ubiquitous and Subjective Data. 1st International Workshop on Pervasive Urban Crowdsensing Architecture and Applications, PUCAA 2013, Zurich, Switzerland -- September 9, 2013. Proceedings. p. New York, NY, USA. ACM (2013).
  [ Abstract ] [ BibTeX ] [ Download ] [ Details ]
   
  An increasing number of platforms like Xively or ThingSpeak are available to manage ubiquitous sensor data enabling the Internet of Things. Strict data formats allow interoperability and informative visualizations, supporting the development of custom user applications. Yet, these strict data formats as well as the common feed-centric approach limit the flexibility of these platforms. We aim at providing a concept that supports data ranging from text-based formats like JSON to images and video footage. Furthermore, we introduce the concept of extensions, which allows to enrich existing data points with additional information, thus, taking a data point centric approach. This enables us to gain semantic and user specific context by attaching subjective data to objective values. This paper provides an overview of our architecture including concept, implementation details and present applications. We distinguish our approach from several other systems and describe two sensing applications namely AirProbe and WideNoise that were implemented for our platform.
  @inproceedings{mueller-2013a, abstract = {An increasing number of platforms like Xively or ThingSpeak are available to manage ubiquitous sensor data enabling the Internet of Things. Strict data formats allow interoperability and informative visualizations, supporting the development of custom user applications. Yet, these strict data formats as well as the common feed-centric approach limit the flexibility of these platforms. We aim at providing a concept that supports data ranging from text-based formats like JSON to images and video footage. Furthermore, we introduce the concept of extensions, which allows to enrich existing data points with additional information, thus, taking a data point centric approach. This enables us to gain semantic and user specific context by attaching subjective data to objective values. This paper provides an overview of our architecture including concept, implementation details and present applications. We distinguish our approach from several other systems and describe two sensing applications namely AirProbe and WideNoise that were implemented for our platform.}, author = {Becker, Martin and Mueller, Juergen and Hotho, Andreas and Stumme, Gerd}, booktitle = {1st International Workshop on Pervasive Urban Crowdsensing Architecture and Applications, PUCAA 2013, Zurich, Switzerland -- September 9, 2013. Proceedings}, note = {Accepted for publication}, pages = {New York, NY, USA}, publisher = {ACM}, title = {A Generic Platform for Ubiquitous and Subjective Data}, year = 2013 }
• 
  Mueller, J., Doerfel, S., Becker, M., Hotho, A., Stumme, G.: Tag Recommendations for SensorFolkSonomies. Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings. CEUR-WS, Aachen, Germany (2013).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  With the rising popularity of smart mobile devices, sensor data-based applications have become more and more popular. Their users record data during their daily routine or specifically for certain events. The application WideNoise Plus allows users to record sound samples and to annotate them with perceptions and tags. The app is being used to document and map the soundscape all over the world. The procedure of recording, including the assignment of tags, has to be as easy-to-use as possible. We therefore discuss the application of tag recommender algorithms in this particular scenario. We show, that this task is fundamentally different from the well-known tag recommendation problem in folksonomies as users do no longer tag fix resources but rather sensory data and impressions. The scenario requires efficient recommender algorithms that are able to run on the mobile device, since Internet connectivity cannot be assumed to be available. Therefore, we evaluate the performance of several tag recommendation algorithms and discuss their applicability in the mobile sensing use-case.
  @inproceedings{mueller2013recommendations, abstract = {With the rising popularity of smart mobile devices, sensor data-based applications have become more and more popular. Their users record data during their daily routine or specifically for certain events. The application WideNoise Plus allows users to record sound samples and to annotate them with perceptions and tags. The app is being used to document and map the soundscape all over the world. The procedure of recording, including the assignment of tags, has to be as easy-to-use as possible. We therefore discuss the application of tag recommender algorithms in this particular scenario. We show, that this task is fundamentally different from the well-known tag recommendation problem in folksonomies as users do no longer tag fix resources but rather sensory data and impressions. The scenario requires efficient recommender algorithms that are able to run on the mobile device, since Internet connectivity cannot be assumed to be available. Therefore, we evaluate the performance of several tag recommendation algorithms and discuss their applicability in the mobile sensing use-case.}, address = {Aachen, Germany}, author = {Mueller, Juergen and Doerfel, Stephan and Becker, Martin and Hotho, Andreas and Stumme, Gerd}, booktitle = {Recommender Systems and the Social Web Workshop at 7th ACM Conference on Recommender Systems, RecSys 2013, Hong Kong, China -- October 12-16, 2013. Proceedings}, publisher = {CEUR-WS}, title = {Tag Recommendations for SensorFolkSonomies}, volume = 1066, year = 2013 }
• 
  Becker, M., Caminiti, S., Fiorella, D., Francis, L., Gravino, P., Haklay, M. (M.), Hotho, A., Loreto, V., Mueller, J., Ricchiuti, F., Servedio, V.D.P., Sîrbu, A., Tria, F.: Awareness and Learning in Participatory Noise Sensing. PLoS ONE. 8, e81638 (2013).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
   
  <p>The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.</p>
  @article{10.1371/journal.pone.0081638, abstract = {<p>The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.</p>}, author = {Becker, Martin and Caminiti, Saverio and Fiorella, Donato and Francis, Louise and Gravino, Pietro and Haklay, Mordechai (Muki) and Hotho, Andreas and Loreto, Vittorio and Mueller, Juergen and Ricchiuti, Ferdinando and Servedio, Vito D. P. and Sîrbu, Alina and Tria, Francesca}, journal = {PLoS ONE}, month = 12, number = 12, pages = {e81638}, publisher = {Public Library of Science}, title = {Awareness and Learning in Participatory Noise Sensing}, volume = 8, year = 2013 }
• 
  Doerfel, S., Hotho, A., Kartal-Aydemir, A., Roßnagel, A., Stumme, G.: Informationelle Selbstbestimmung Im Web 2.0 Chancen Und Risiken Sozialer Verschlagwortungssysteme. Vieweg + Teubner Verlag (2013).
  [ BibTeX ] [ URL ] [ Details ]
   
  @book{doerfel2013informationelle, author = {Doerfel, Stephan and Hotho, Andreas and Kartal-Aydemir, Aliye and Roßnagel, Alexander and Stumme, Gerd}, publisher = {Vieweg + Teubner Verlag}, title = {Informationelle Selbstbestimmung Im Web 2.0 Chancen Und Risiken Sozialer Verschlagwortungssysteme}, year = 2013 }
• 
  Atzmueller, M., Chin, A., Helic, D., Hotho, A. eds: Ubiquitous Social Media Analysis Third International Workshops, MUSE 2012, Bristol, UK, September 24, 2012, and MSM 2012, Milwaukee, WI, USA, June 25, 2012, Revised Selected Papers. Imprint: Springer, Berlin, Heidelberg (2013).
  [ BibTeX ] [ URL ] [ Details ]
   
  @book{atzmueller2013ubiquitous, address = {Berlin, Heidelberg}, editor = {Atzmueller, Martin and Chin, Alvin and Helic, Denis and Hotho, Andreas}, publisher = {Imprint: Springer}, title = {Ubiquitous Social Media Analysis Third International Workshops, MUSE 2012, Bristol, UK, September 24, 2012, and MSM 2012, Milwaukee, WI, USA, June 25, 2012, Revised Selected Papers}, year = 2013 }
• 
  Mobasher, B., Jannach, D., Geyer, W., Freyne, J., Hotho, A., Anand, S.S., Guy, I. eds: Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013. CEUR-WS.org (2013).
  [ BibTeX ] [ URL ] [ Details ]
   
  @proceedings{conf/recsys/2013rsweb, booktitle = {RSWeb@RecSys}, editor = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Freyne, Jill and Hotho, Andreas and Anand, Sarabjot Singh and Guy, Ido}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013.}, volume = 1066, year = 2013 }
• 
  Singer, P., Niebler, T., Strohmaier, M., Hotho, A.: Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia. International Journal on Semantic Web and Information Systems (IJSWIS). 9, 41--70 (2013).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
   
  In this article, the authors present a novel approach for computing semantic relatedness and conduct a large-scale study of it on Wikipedia. Unlike existing semantic analysis methods that utilize Wikipedia’s content or link structure, the authors propose to use human navigational paths on Wikipedia for this task. The authors obtain 1.8 million human navigational paths from a semi-controlled navigation experiment – a Wikipedia-based navigation game, in which users are required to find short paths between two articles in a given Wikipedia article network. The authors’ results are intriguing: They suggest that (i) semantic relatedness computed from human navigational paths may be more precise than semantic relatedness computed from Wikipedia’s plain link structure alone and (ii) that not all navigational paths are equally useful. Intelligent selection based on path characteristics can improve accuracy. The authors’ work makes an argument for expanding the existing arsenal of data sources for calculating semantic relatedness and to consider the utility of human navigational paths for this task.
  @article{singer2013computing, abstract = {In this article, the authors present a novel approach for computing semantic relatedness and conduct a large-scale study of it on Wikipedia. Unlike existing semantic analysis methods that utilize Wikipedia’s content or link structure, the authors propose to use human navigational paths on Wikipedia for this task. The authors obtain 1.8 million human navigational paths from a semi-controlled navigation experiment – a Wikipedia-based navigation game, in which users are required to find short paths between two articles in a given Wikipedia article network. The authors’ results are intriguing: They suggest that (i) semantic relatedness computed from human navigational paths may be more precise than semantic relatedness computed from Wikipedia’s plain link structure alone and (ii) that not all navigational paths are equally useful. Intelligent selection based on path characteristics can improve accuracy. The authors’ work makes an argument for expanding the existing arsenal of data sources for calculating semantic relatedness and to consider the utility of human navigational paths for this task.}, author = {Singer, Philipp and Niebler, Thomas and Strohmaier, Markus and Hotho, Andreas}, journal = {International Journal on Semantic Web and Information Systems (IJSWIS)}, number = 4, pages = {41--70}, publisher = {IGI Global}, title = {Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia}, volume = 9, year = 2013 }
• 
  Kluegl, P., Toepfer, M., Lemmerich, F., Hotho, A., Puppe, F.: Exploiting Structural Consistencies with Stacked Conditional Random Fields. Mathematical Methodologies in Pattern Recognition and Machine Learning Springer Proceedings in Mathematics & Statistics. 30, 111-125 (2013).
  [ Abstract ] [ BibTeX ] [ Details ]
   
  Conditional Random Fields (CRF) are popular methods for labeling unstructured or textual data. Like many machine learning approaches, these undirected graphical models assume the instances to be independently distributed. However, in real-world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. We apply rule learning collectively on the predictions of an initial CRF for one context to acquire descriptions of its specific properties. Then, we utilize these descriptions as dynamic and high quality features in an additional (stacked) CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.
  @article{kluegl2013exploiting, abstract = {Conditional Random Fields (CRF) are popular methods for labeling unstructured or textual data. Like many machine learning approaches, these undirected graphical models assume the instances to be independently distributed. However, in real-world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. We apply rule learning collectively on the predictions of an initial CRF for one context to acquire descriptions of its specific properties. Then, we utilize these descriptions as dynamic and high quality features in an additional (stacked) CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.}, author = {Kluegl, Peter and Toepfer, Martin and Lemmerich, Florian and Hotho, Andreas and Puppe, Frank}, journal = {Mathematical Methodologies in Pattern Recognition and Machine Learning Springer Proceedings in Mathematics & Statistics}, pages = {111-125}, title = {Exploiting Structural Consistencies with Stacked Conditional Random Fields}, volume = 30, year = 2013 }

2012 [ nach oben ]

• 
  Klügl, P., Toepfer, M., Lemmerich, F., Hotho, A., Puppe, F.: Stacked Conditional Random Fields Exploiting Structural Consistencies. In: Carmona, P.L., Sánchez, J.S., and Fred, A. (eds.) Proceedings of 1st International Conference on Pattern Recognition Applications and Methods ICPRAM. pp. 240-248. SciTePress, Vilamoura, Algarve, Portugal (2012).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  Conditional Random Fields CRF are popular methods for labeling unstructured or textual data. Like many machine learning approaches these undirected graphical models assume the instances to be independently distributed. However, in real world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. The approach incorporates three successive steps of inference: First, an initial CRF processes single instances as usual. Next, we apply rule learning collectively on all labeled outputs of one context to acquire descriptions of its specific properties. Finally, we utilize these descriptions as dynamic and high quality features in an additional stacked CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.
  @inproceedings{kluegl2012stacked, abstract = {Conditional Random Fields CRF are popular methods for labeling unstructured or textual data. Like many machine learning approaches these undirected graphical models assume the instances to be independently distributed. However, in real world applications data is grouped in a natural way, e.g., by its creation context. The instances in each group often share additional structural consistencies. This paper proposes a domain-independent method for exploiting these consistencies by combining two CRFs in a stacked learning framework. The approach incorporates three successive steps of inference: First, an initial CRF processes single instances as usual. Next, we apply rule learning collectively on all labeled outputs of one context to acquire descriptions of its specific properties. Finally, we utilize these descriptions as dynamic and high quality features in an additional stacked CRF. The presented approach is evaluated with a real-world dataset for the segmentation of references and achieves a significant reduction of the labeling error.}, address = {Vilamoura, Algarve, Portugal}, author = {Klügl, Peter and Toepfer, Martin and Lemmerich, Florian and Hotho, Andreas and Puppe, Frank}, booktitle = {Proceedings of 1st International Conference on Pattern Recognition Applications and Methods ICPRAM}, editor = {Carmona, Pedro Latorre and Sánchez, J. Salvador and Fred, Ana}, pages = {240-248}, publisher = {SciTePress}, title = {Stacked Conditional Random Fields Exploiting Structural Consistencies}, year = 2012 }
• 
  Said, A., Tikk, D., Hotho, A.: The challenge of recommender systems challenges. In: Cunningham, P., Hurley, N.J., Guy, I., and Anand, S.S. (eds.) RecSys. pp. 9-10. ACM (2012).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{conf/recsys/SaidTH12, author = {Said, Alan and Tikk, Domonkos and Hotho, Andreas}, booktitle = {RecSys}, crossref = {conf/recsys/2012}, editor = {Cunningham, Padraig and Hurley, Neil J. and Guy, Ido and Anand, Sarabjot Singh}, pages = {9-10}, publisher = {ACM}, title = {The challenge of recommender systems challenges.}, year = 2012 }
• 
  Jäschke, R., Hotho, A., Mitzlaff, F., Stumme, G.: Challenges in Tag Recommendations for Collaborative Tagging Systems. In: Pazos Arias, J.J., Fernández Vilas, A., and Díaz Redondo, R.P. (eds.) Recommender Systems for the Social Web. p. 65--87. Springer, Berlin/Heidelberg (2012).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.
  @incollection{jaeschke2012challenges, abstract = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area.}, address = {Berlin/Heidelberg}, author = {Jäschke, Robert and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {Recommender Systems for the Social Web}, editor = {Pazos Arias, José J. and Fernández Vilas, Ana and Díaz Redondo, Rebeca P.}, pages = {65--87}, publisher = {Springer}, series = {Intelligent Systems Reference Library}, title = {Challenges in Tag Recommendations for Collaborative Tagging Systems}, volume = 32, year = 2012 }
• 
  Balby Marinho, L., Hotho, A., Jäschke, R., Nanopoulos, A., Rendle, S., Schmidt-Thieme, L., Stumme, G., Symeonidis, P.: Recommender Systems for Social Tagging Systems. Springer (2012).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.
  @book{balbymarinho2012recommender, abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.}, author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.}, month = {feb}, publisher = {Springer}, series = {SpringerBriefs in Electrical and Computer Engineering}, title = {Recommender Systems for Social Tagging Systems}, year = 2012 }
• 
  Mobasher, B., Jannach, D., Geyer, W., Hotho, A.: RSWeb '12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web. ACM, Dublin, Ireland (2012).
  [ Abstract ] [ BibTeX ] [ Details ]
   
  The new opportunities for applying recommendation techniques within Social Web platforms and applications as well as the various new sources of information which have become available in the Web 2.0 and can be incorporated in future recommender applications are a strong driving factor in current recommender system research for various reasons:</p> <p>(1) Social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation.</p> <p>(2) New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders cannot only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development.</p> <p>(3) Recommender technology can assist Social Web systems through increasing adoption and participation and sustaining membership. Through targeted and timely intervention which stimulates traffic and interaction, recommender technology can play its role in sustaining the success of the Social Web.</p> <p>(4) The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people and can require more interactive and richer recommender systems user interfaces.</p> <p>The technical papers appearing in these proceedings aim to explore and understand challenges and new opportunities for recommender systems in the Social Web and were selected in a formal review process by an international program committee.</p> <p>Overall, we received 13 paper submissions from 12 different countries, out of which 7 long papers and 1 short paper were selected for presentation and inclusion in the proceedings. The submitted papers addressed a variety of topics related to Social Web recommender systems from the use of microblogging data for personalization over new tag recommendation approaches to social media-based personalization of news.
  @proceedings{Mobasher:2012:2365934, abstract = {The new opportunities for applying recommendation techniques within Social Web platforms and applications as well as the various new sources of information which have become available in the Web 2.0 and can be incorporated in future recommender applications are a strong driving factor in current recommender system research for various reasons:</p> <p>(1) Social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation.</p> <p>(2) New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders cannot only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development.</p> <p>(3) Recommender technology can assist Social Web systems through increasing adoption and participation and sustaining membership. Through targeted and timely intervention which stimulates traffic and interaction, recommender technology can play its role in sustaining the success of the Social Web.</p> <p>(4) The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people and can require more interactive and richer recommender systems user interfaces.</p> <p>The technical papers appearing in these proceedings aim to explore and understand challenges and new opportunities for recommender systems in the Social Web and were selected in a formal review process by an international program committee.</p> <p>Overall, we received 13 paper submissions from 12 different countries, out of which 7 long papers and 1 short paper were selected for presentation and inclusion in the proceedings. The submitted papers addressed a variety of topics related to Social Web recommender systems from the use of microblogging data for personalization over new tag recommendation approaches to social media-based personalization of news.}, address = {New York, NY, USA}, author = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Hotho, Andreas}, note = 609126, publisher = {ACM}, title = {RSWeb '12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, year = 2012 }
• 
  Landia, N., Anand, S.S., Hotho, A., Jäschke, R., Doerfel, S., Mitzlaff, F.: Extending FolkRank with content data. Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web. p. 1--8. ACM, Dublin, Ireland (2012).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.</p> <p>Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.
  @inproceedings{Landia:2012:EFC:2365934.2365936, abstract = {Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content. In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.</p> <p>Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.}, address = {New York, NY, USA}, author = {Landia, Nikolas and Anand, Sarabjot Singh and Hotho, Andreas and Jäschke, Robert and Doerfel, Stephan and Mitzlaff, Folke}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, pages = {1--8}, publisher = {ACM}, series = {RSWeb '12}, title = {Extending FolkRank with content data}, year = 2012 }
• 
  Doerfel, S., Jäschke, R., Hotho, A., Stumme, G.: Leveraging publication metadata and social data into FolkRank for scientific publication recommendation. Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web. p. 9--16. ACM, Dublin, Ireland (2012).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.
  @inproceedings{Doerfel:2012:LPM:2365934.2365937, abstract = {The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.}, address = {New York, NY, USA}, author = {Doerfel, Stephan and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web}, pages = {9--16}, publisher = {ACM}, series = {RSWeb '12}, title = {Leveraging publication metadata and social data into FolkRank for scientific publication recommendation}, year = 2012 }
• 
  Hotho, A.: Publikationen im Web 2.0. Informatik-Spektrum. 1-5 (2012).
  [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  @article{hotho2012publikationen, address = {Berlin / Heidelberg}, author = {Hotho, Andreas}, journal = {Informatik-Spektrum}, pages = {1-5}, publisher = {Springer}, title = {Publikationen im Web 2.0}, year = 2012 }
• 
  Atzmueller, M., Becker, M., Doerfel, S., Kibanov, M., Hotho, A., Macek, B.-E., Mitzlaff, F., Mueller, J., Scholz, C., Stumme, G.: Ubicon: Observing Social and Physical Activities. IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012, Besançon, France, 20-23 November, 2012. IEEE, Washington, DC, USA (2012).
  [ Abstract ] [ BibTeX ] [ Download ] [ Details ]
   
  The connection of ubiquitous and social computing is an emerging research area which is combining two prominent areas of computer science. In this paper, we tackle this topic from different angles: We describe data mining methods for ubiquitous and social data, specifically focusing on physical and social activities, and provide exemplary analysis results. Furthermore, we give an overview on the Ubicon platform which provides a framework for the creation and hosting of ubiquitous and social applications for diverse tasks and projects. Ubicon features the collection and analysis of both physical and social activities of users for enabling inter-connected applications in ubiquitous and social contexts. We summarize three real-world systems built on top of Ubicon, and exemplarily discuss the according mining and analysis aspects.
  @inproceedings{mueller-2012, abstract = {The connection of ubiquitous and social computing is an emerging research area which is combining two prominent areas of computer science. In this paper, we tackle this topic from different angles: We describe data mining methods for ubiquitous and social data, specifically focusing on physical and social activities, and provide exemplary analysis results. Furthermore, we give an overview on the Ubicon platform which provides a framework for the creation and hosting of ubiquitous and social applications for diverse tasks and projects. Ubicon features the collection and analysis of both physical and social activities of users for enabling inter-connected applications in ubiquitous and social contexts. We summarize three real-world systems built on top of Ubicon, and exemplarily discuss the according mining and analysis aspects.}, address = {Washington, DC, USA}, author = {Atzmueller, Martin and Becker, Martin and Doerfel, Stephan and Kibanov, Mark and Hotho, Andreas and Macek, Björn-Elmar and Mitzlaff, Folke and Mueller, Juergen and Scholz, Christoph and Stumme, Gerd}, booktitle = {IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012, Besançon, France, 20-23 November, 2012}, publisher = {IEEE}, title = {Ubicon: Observing Social and Physical Activities}, year = 2012 }
• 
  Mobasher, B., Jannach, D., Geyer, W., Hotho, A.: 4th ACM RecSys workshop on recommender systems and the social web. In: Cunningham, P., Hurley, N.J., Guy, I., and Anand, S.S. (eds.) RecSys. pp. 345-346. ACM (2012).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{conf/recsys/MobasherJGH12, author = {Mobasher, Bamshad and Jannach, Dietmar and Geyer, Werner and Hotho, Andreas}, booktitle = {RecSys}, crossref = {conf/recsys/2012}, editor = {Cunningham, Padraig and Hurley, Neil J. and Guy, Ido and Anand, Sarabjot Singh}, pages = {345-346}, publisher = {ACM}, title = {4th ACM RecSys workshop on recommender systems and the social web.}, year = 2012 }
• 
  Krause, B., Lerch, H., Hotho, A., Roßnagel, A., Stumme, G.: Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy. Informatik Spektrum. 35, 12-23 (2012).
  [ BibTeX ] [ URL ] [ Details ]
   
  @article{journals/insk/KrauseLHRS12, author = {Krause, Beate and Lerch, Hana and Hotho, Andreas and Roßnagel, Alexander and Stumme, Gerd}, journal = {Informatik Spektrum}, number = 1, pages = {12-23}, title = {Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy.}, volume = 35, year = 2012 }
• 
  Atzmueller, M., Doerfel, S., Hotho, A., Mitzlaff, F., Stumme, G.: Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles. Modeling and Mining Ubiquitous Social Media. Springer Verlag, Heidelberg, Germany (2012).
  [ BibTeX ] [ URL ] [ Details ]
   
  @incollection{ADHMS:12, address = {Heidelberg, Germany}, author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {Modeling and Mining Ubiquitous Social Media}, publisher = {Springer Verlag}, series = {LNAI}, title = {Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles}, volume = 7472, year = 2012 }
• 
  Atzmueller, M., Chin, A., Helic, D., Hotho, A. eds: Modeling and Mining Ubiquitous Social Media. Springer Verlag, Heidelberg, Germany (2012).
  [ BibTeX ] [ URL ] [ Details ]
   
  @book{ACHH:12, address = {Heidelberg, Germany}, editor = {Atzmueller, Martin and Chin, Alvin and Helic, Denis and Hotho, Andreas}, publisher = {Springer Verlag}, series = {Lecture Notes in Computer Science}, title = {Modeling and Mining Ubiquitous Social Media}, volume = 7472, year = 2012 }
• 
  Atzmueller, M., Hotho, A. eds: Proceedings of the Third International Workshop on Mining Ubiquitous and Social Environments (MUSE 2012). Workshop Notes, Bristol, UK (2012).
  [ BibTeX ] [ URL ] [ Details ]
   
  @book{AH:12, address = {Bristol, UK}, editor = {Atzmueller, Martin and Hotho, Andreas}, publisher = {Workshop Notes}, title = {Proceedings of the Third International Workshop on Mining Ubiquitous and Social Environments (MUSE 2012)}, year = 2012 }
• 
  Klügl, P., Toepfer, M., Lemmerich, F., Hotho, A., Puppe, F.: Collective Information Extraction with Context-Specific Consistencies. In: Flach, P.A., Bie, T.D., and Cristianini, N. (eds.) ECML/PKDD (1). pp. 728-743. Springer (2012).
  [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  @inproceedings{conf/pkdd/KluglTLHP12, author = {Klügl, Peter and Toepfer, Martin and Lemmerich, Florian and Hotho, Andreas and Puppe, Frank}, booktitle = {ECML/PKDD (1)}, crossref = {conf/pkdd/2012-1}, editor = {Flach, Peter A. and Bie, Tijl De and Cristianini, Nello}, pages = {728-743}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Collective Information Extraction with Context-Specific Consistencies.}, volume = 7523, year = 2012 }

2011 [ nach oben ]

• 
  Illig, J., Hotho, A., Jäschke, R., Stumme, G.: A Comparison of Content-Based Tag Recommendations in Folksonomy Systems. In: Wolff, K.E., Palchunov, D.E., Zagoruiko, N.G., and Andelfinger, U. (eds.) Knowledge Processing and Data Analysis. p. 136--149. Springer, Berlin/Heidelberg (2011).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.
  @inproceedings{illig2009comparison, abstract = {Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i.e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in a cold-start scenario on a large real-world dataset.}, address = {Berlin/Heidelberg}, author = {Illig, Jens and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, booktitle = {Knowledge Processing and Data Analysis}, editor = {Wolff, Karl Erich and Palchunov, Dmitry E. and Zagoruiko, Nikolay G. and Andelfinger, Urs}, pages = {136--149}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {A Comparison of Content-Based Tag Recommendations in Folksonomy Systems}, volume = 6581, year = 2011 }
• 
  Atzmueller, M., Doerfel, S., Hotho, A., Mitzlaff, F., Stumme, G.: Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players. Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011 (2011).
  [ BibTeX ] [ Details ]
   
  @inproceedings{ADHMS:11, author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011}, title = {Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players}, year = 2011 }
• 
  Bullock, B.N., Lerch, H., Rossnagel, A., Hotho, A., Stumme, G.: Privacy-aware spam detection in social bookmarking systems. Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies. p. 15:1--15:8. ACM, Graz, Austria (2011).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  With the increased popularity of Web 2.0 services in the last years data privacy has become a major concern for users. The more personal data users reveal, the more difficult it becomes to control its disclosure in the web. However, for Web 2.0 service providers, the data provided by users is a valuable source for offering effective, personalised data mining services. One major application is the detection of spam in social bookmarking systems: in order to prevent a decrease of content quality, providers need to distinguish spammers and exclude them from the system. They thereby experience a conflict of interests: on the one hand, they need to identify spammers based on the information they collect about users, on the other hand, they need to respect privacy concerns and process as few personal data as possible. It would therefore be of tremendous help for system developers and users to know which personal data are needed for spam detection and which can be ignored. In this paper we address these questions by presenting a data privacy aware feature engineering approach. It consists of the design of features for spam classification which are evaluated according to both, performance and privacy conditions. Experiments using data from the social bookmarking system BibSonomy show that both conditions must not exclude each other.
  @inproceedings{bullock2011privacyaware, abstract = {With the increased popularity of Web 2.0 services in the last years data privacy has become a major concern for users. The more personal data users reveal, the more difficult it becomes to control its disclosure in the web. However, for Web 2.0 service providers, the data provided by users is a valuable source for offering effective, personalised data mining services. One major application is the detection of spam in social bookmarking systems: in order to prevent a decrease of content quality, providers need to distinguish spammers and exclude them from the system. They thereby experience a conflict of interests: on the one hand, they need to identify spammers based on the information they collect about users, on the other hand, they need to respect privacy concerns and process as few personal data as possible. It would therefore be of tremendous help for system developers and users to know which personal data are needed for spam detection and which can be ignored. In this paper we address these questions by presenting a data privacy aware feature engineering approach. It consists of the design of features for spam classification which are evaluated according to both, performance and privacy conditions. Experiments using data from the social bookmarking system BibSonomy show that both conditions must not exclude each other.}, address = {New York, NY, USA}, author = {Bullock, Beate Navarro and Lerch, Hana and Rossnagel, Alexander and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies}, pages = {15:1--15:8}, publisher = {ACM}, series = {i-KNOW '11}, title = {Privacy-aware spam detection in social bookmarking systems}, year = 2011 }
• 
  Atzmueller, M., Hotho, A., Strohmaier, M., Chin, A. eds: Analysis of Social Media and Ubiquitous Data - International Workshops MSM 2010, Toronto, Canada, June 13, 2010, and MUSE 2010, Barcelona, Spain, September 20, 2010, Revised Selected Papers. Springer (2011).
  [ BibTeX ] [ URL ] [ Details ]
   
  @proceedings{conf/ht/2010msmmuse, booktitle = {MSM/MUSE}, editor = {Atzmueller, Martin and Hotho, Andreas and Strohmaier, Markus and Chin, Alvin}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Analysis of Social Media and Ubiquitous Data - International Workshops MSM 2010, Toronto, Canada, June 13, 2010, and MUSE 2010, Barcelona, Spain, September 20, 2010, Revised Selected Papers}, volume = 6904, year = 2011 }
• 
  Mitzlaff, F., Atzmueller, M., Stumme, G., Hotho, A.: On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission). Proc. LWA 2013 (KDML Special Track). University of Bamberg, Bamberg, Germany (2011).
  [ BibTeX ] [ Details ]
   
  @inproceedings{MASH:13b, address = {Bamberg, Germany}, author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas}, booktitle = {Proc. LWA 2013 (KDML Special Track)}, publisher = {University of Bamberg}, title = {On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission)}, year = 2011 }
• 
  Bloehdorn, S., Blohm, S., Cimiano, P., Giesbrecht, E., Hotho, A., Lösch, U., Mädche, A., Mönch, E., Sorg, P., Staab, S., Völker, J.: Combining Data-Driven and Semantic Approaches for Text Mining. In: Fensel, D. (ed.) Foundations for the Web of Information and Services. pp. 115-142. Springer (2011).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{conf/birthday/BloehdornBCGHLMMSSV11, author = {Bloehdorn, Stephan and Blohm, Sebastian and Cimiano, Philipp and Giesbrecht, Eugenie and Hotho, Andreas and Lösch, Uta and Mädche, Alexander and Mönch, Eddie and Sorg, Philipp and Staab, Steffen and Völker, Johanna}, booktitle = {Foundations for the Web of Information and Services}, crossref = {conf/birthday/2011studer}, editor = {Fensel, Dieter}, pages = {115-142}, publisher = {Springer}, title = {Combining Data-Driven and Semantic Approaches for Text Mining.}, year = 2011 }
• 
  Cattuto, C., Hotho, A.: Introduction to the Special Issue on Social Linking and Hypermedia. New Review of Hypermedia and Multimedia. 17, 241-242 (2011).
  [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  @article{doi:10.1080/13614568.2011.641407, author = {Cattuto, Ciro and Hotho, Andreas}, journal = {New Review of Hypermedia and Multimedia}, number = 3, pages = {241-242}, title = {Introduction to the Special Issue on Social Linking and Hypermedia}, volume = 17, year = 2011 }
• 
  Hotho, A., Stumme, G.: From Semantic Web Mining to Social and Ubiquitous Mining - A Subjective View on Past, Current, and Future Research. In: Fensel, D. (ed.) Foundations for the Web of Information and Services. pp. 143-153. Springer (2011).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{conf/birthday/HothoS11, author = {Hotho, Andreas and Stumme, Gerd}, booktitle = {Foundations for the Web of Information and Services}, crossref = {conf/birthday/2011studer}, editor = {Fensel, Dieter}, pages = {143-153}, publisher = {Springer}, title = {From Semantic Web Mining to Social and Ubiquitous Mining - A Subjective View on Past, Current, and Future Research.}, year = 2011 }
• 
  Atzmueller, M., Benz, D., Doerfel, S., Hotho, A., Jäschke, R., Macek, B.E., Mitzlaff, F., Scholz, C., Stumme, G.: Enhancing Social Interactions at Conferences. it - Information Technology. 53, 101--107 (2011).
  [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  @article{martin2011enhancing, author = {Atzmueller, Martin and Benz, Dominik and Doerfel, Stephan and Hotho, Andreas and Jäschke, Robert and Macek, Bjoern Elmar and Mitzlaff, Folke and Scholz, Christoph and Stumme, Gerd}, booktitle = {it - Information Technology}, journal = {it - Information Technology}, month = {may}, number = 3, pages = {101--107}, publisher = {Oldenbourg Wissenschaftsverlag GmbH}, title = {Enhancing Social Interactions at Conferences}, volume = 53, year = 2011 }
• 
  Freyne, J., Anand, S.S., Guy, I., Hotho, A.: 3rd workshop on recommender systems and the social web. Proceedings of the fifth ACM conference on Recommender systems. p. 383--384. ACM, Chicago, Illinois, USA (2011).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  The exponential growth of the social web poses challenges and new opportunities for recommender systems. The social web has turned information consumers into active contributors creating massive amounts of information. Finding relevant and interesting content at the right time and in the right context is challenging for existing recommender approaches. At the same time, social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. The Social Web provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.</p> <p>The goal of this one day workshop was to bring together researchers and practitioners to explore, discuss, and understand challenges and new opportunities for Recommender Systems and the Social Web. The workshop consisted both of technical sessions, in which selected participants presented their results or ongoing research, as well as informal breakout sessions on more focused topics.</p> <p>Papers discussing various aspects of recommender system in the Social Web were submitted and selected for presentation and discussion in the workshop in a formal reviewing process: Case studies and novel fielded social recommender applications; Economy of community-based systems: Using recommenders to encourage users to contribute and sustain participation.; Social network and folksonomy development: Recommending friends, tags, bookmarks, blogs, music, communities etc.; Recommender systems mash-ups, Web 2.0 user interfaces, rich media recommender systems; Collaborative knowledge authoring, collective intelligence; Recommender applications involving users or groups directly in the recommendation process; Exploiting folksonomies, social network information, interaction, user context and communities or groups for recommendations; Trust and reputation aware social recommendations; Semantic Web recommender systems, use of ontologies or microformats; Empirical evaluation of social recommender techniques, success and failure measures</p> <p>Full workshop details are available at http://www.dcs.warwick.ac.uk/~ssanand/RSWeb11/index.htm
  @inproceedings{Freyne:2011:WRS:2043932.2044014, abstract = {The exponential growth of the social web poses challenges and new opportunities for recommender systems. The social web has turned information consumers into active contributors creating massive amounts of information. Finding relevant and interesting content at the right time and in the right context is challenging for existing recommender approaches. At the same time, social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. The Social Web provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.</p> <p>The goal of this one day workshop was to bring together researchers and practitioners to explore, discuss, and understand challenges and new opportunities for Recommender Systems and the Social Web. The workshop consisted both of technical sessions, in which selected participants presented their results or ongoing research, as well as informal breakout sessions on more focused topics.</p> <p>Papers discussing various aspects of recommender system in the Social Web were submitted and selected for presentation and discussion in the workshop in a formal reviewing process: Case studies and novel fielded social recommender applications; Economy of community-based systems: Using recommenders to encourage users to contribute and sustain participation.; Social network and folksonomy development: Recommending friends, tags, bookmarks, blogs, music, communities etc.; Recommender systems mash-ups, Web 2.0 user interfaces, rich media recommender systems; Collaborative knowledge authoring, collective intelligence; Recommender applications involving users or groups directly in the recommendation process; Exploiting folksonomies, social network information, interaction, user context and communities or groups for recommendations; Trust and reputation aware social recommendations; Semantic Web recommender systems, use of ontologies or microformats; Empirical evaluation of social recommender techniques, success and failure measures</p> <p>Full workshop details are available at http://www.dcs.warwick.ac.uk/~ssanand/RSWeb11/index.htm}, address = {New York, NY, USA}, author = {Freyne, Jill and Anand, Sarabjot Singh and Guy, Ido and Hotho, Andreas}, booktitle = {Proceedings of the fifth ACM conference on Recommender systems}, pages = {383--384}, publisher = {ACM}, series = {RecSys '11}, title = {3rd workshop on recommender systems and the social web}, year = 2011 }
• 
  Marinho, L.B., Nanopoulos, A., Schmidt-Thieme, L., Jäschke, R., Hotho, A., Stumme, G., Symeonidis, P.: Social Tagging Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B., and Kantor, P.B. (eds.) Recommender Systems Handbook. pp. 615-644. Springer (2011).
  [ BibTeX ] [ URL ] [ Details ]
   
  @incollection{reference/rsh/MarinhoNSJHSS11, author = {Marinho, Leandro Balby and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis}, booktitle = {Recommender Systems Handbook}, crossref = {reference/rsh/2011}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, pages = {615-644}, publisher = {Springer}, title = {Social Tagging Recommender Systems.}, year = 2011 }
• 
  Benz, D., Körner, C., Hotho, A., Stumme, G., Strohmaier, M.: One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata. Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation (2011).
  [ BibTeX ] [ Details ]
   
  @inproceedings{benz2011measuring, author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus}, booktitle = {Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation}, title = {One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata}, year = 2011 }
• 
  Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: Community Assessment using Evidence Networks. Analysis of Social Media and Ubiquitous Data (2011).
  [ BibTeX ] [ Details ]
   
  @inproceedings{mitzlaff2011community, author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Analysis of Social Media and Ubiquitous Data}, series = {LNAI}, title = {Community Assessment using Evidence Networks}, volume = 6904, year = 2011 }
• 
  Atzmueller, M., Hotho, A. eds: Proceedings of the 2011 International Workshop on Mining Ubiquitous and Social Environments (MUSE 2011). ECML/PKDD 2011, Athens, Greece (2011).
  [ BibTeX ] [ Details ]
   
  @book{AH:11, address = {Athens, Greece}, editor = {Atzmueller, Martin and Hotho, Andreas}, publisher = {ECML/PKDD 2011}, title = {Proceedings of the 2011 International Workshop on Mining Ubiquitous and Social Environments (MUSE 2011)}, year = 2011 }
• 
  Toepfer, M., Kluegl, P., Hotho, A., Puppe, F.: Segmentation of References with Skip-Chain Conditional Random Fields for Consistent Label Transitions. Workshop Notes of the LWA 2011 - Learning, Knowledge, Adaptation (2011).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{toepfer2011segmentation, author = {Toepfer, Martin and Kluegl, Peter and Hotho, Andreas and Puppe, Frank}, booktitle = {Workshop Notes of the LWA 2011 - Learning, Knowledge, Adaptation}, title = {Segmentation of References with Skip-Chain Conditional Random Fields for Consistent Label Transitions}, year = 2011 }
• 
  Burke, R., Gemmell, J., Hotho, A., Jäschke, R.: Recommendation in the Social Web. AI Magazine. 32, 46--56 (2011).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.
  @article{burke2011recommendation, abstract = {Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.}, author = {Burke, Robin and Gemmell, Jonathan and Hotho, Andreas and Jäschke, Robert}, journal = {AI Magazine}, number = 3, pages = {46--56}, publisher = {Association for the Advancement of Artificial Intelligence}, title = {Recommendation in the Social Web}, volume = 32, year = 2011 }
• 
  Atzmueller, M., Benz, D., Hotho, A., Stumme, G.: Towards Mining Semantic Maturity in Social Bookmarking Systems. In: Passant, A., Fernández, S., Breslin, J., and Bojārs, U. (eds.) Proceedings of the 4th international workshop on Social Data on the Web (SDoW2011) (2011).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{atzmueller2011towards, author = {Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 4th international workshop on Social Data on the Web (SDoW2011)}, editor = {Passant, Alexandre and Fernández, Sergio and Breslin, John and Bojārs, Uldis}, title = {Towards Mining Semantic Maturity in Social Bookmarking Systems}, year = 2011 }
• 
  Scholz, C., Doerfel, S., Atzmueller, M., Hotho, A., Stumme, G.: Resource-Aware On-Line RFID Localization Using Proximity Data. Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation (2011).
  [ BibTeX ] [ Details ]
   
  @inproceedings{scholz2011resourceaware, author = {Scholz, Christoph and Doerfel, Stephan and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd}, booktitle = {Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation}, title = {Resource-Aware On-Line RFID Localization Using Proximity Data}, year = 2011 }
• 
  Bullock, B.N., Jäschke, R., Hotho, A.: Tagging data as implicit feedback for learning-to-rank. Proceedings of the ACM WebSci'11 (2011).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{bullock2011tagging, author = {Bullock, Beate Navarro and Jäschke, Robert and Hotho, Andreas}, booktitle = {Proceedings of the ACM WebSci'11}, month = {June}, title = {Tagging data as implicit feedback for learning-to-rank}, year = 2011 }
• 
  Benz, D., Körner, C., Hotho, A., Stumme, G., Strohmaier, M.: One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., Pan, J., and Leenheer, P.D. (eds.) Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011). , Heraklion, Crete (2011).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.
  @inproceedings{benz2011measuring, abstract = {Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.}, address = {Heraklion, Crete}, author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus}, booktitle = {Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011)}, editor = {Antoniou, Grigoris and Grobelnik, Marko and Simperl, Elena and Parsia, Bijan and Plexousakis, Dimitris and Pan, Jeff and Leenheer, Pieter De}, month = {may}, title = {One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata}, year = 2011 }
• 
  Atzmueller, M., Doerfel, S., Hotho, A., Mitzlaff, F., Stumme, G.: Face-to-Face Contacts during LWA 2010 - Communities, Roles, and Key Players. Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation (2011).
  [ BibTeX ] [ Details ]
   
  @inproceedings{atzmueller2011facetoface, author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd}, booktitle = {Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation}, title = {Face-to-Face Contacts during LWA 2010 - Communities, Roles, and Key Players}, year = 2011 }
• 
  Scholz, C., Doerfel, S., Atzmueller, M., Hotho, A., Stumme, G.: Resource-Aware On-Line RFID Localization Using Proximity Data. ECML/PKDD (3). pp. 129-144 (2011).
  [ BibTeX ] [ Details ]
   
  @inproceedings{DBLP:conf/pkdd/ADHSS11, author = {Scholz, Christoph and Doerfel, Stephan and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd}, booktitle = {ECML/PKDD (3)}, note = {misc = 27}, pages = {129-144}, title = {Resource-Aware On-Line RFID Localization Using Proximity Data}, year = 2011 }

2010 [ nach oben ]

• 
  Benz, D., Hotho, A., Jäschke, R., Krause, B., Stumme, G.: Query Logs as Folksonomies. Datenbank-Spektrum. 10, 15--24 (2010).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well.
  @article{dominik2010query, abstract = {Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well.}, author = {Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Stumme, Gerd}, journal = {Datenbank-Spektrum}, month = {#jun#}, number = 1, pages = {15--24}, title = {Query Logs as Folksonomies}, volume = 10, year = 2010 }
• 
  Benz, D., Hotho, A., Jäschke, R., Krause, B., Mitzlaff, F., Schmitz, C., Stumme, G.: The Social Bookmark and Publication Management System BibSonomy. The VLDB Journal. 19, 849--875 (2010).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
   
  Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.
  @article{benz2010social, abstract = {Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.}, address = {Berlin / Heidelberg}, author = {Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Mitzlaff, Folke and Schmitz, Christoph and Stumme, Gerd}, journal = {The VLDB Journal}, month = {dec}, number = 6, pages = {849--875}, publisher = {Springer}, title = {The Social Bookmark and Publication Management System BibSonomy}, volume = 19, year = 2010 }
• 
  Atzmueller, M., Hotho, A. eds: Proceedings of the 2010 Workshop on Mining Ubiquitous and Social Environments (MUSE 2010). ECML/PKDD 2010, Barcelona, Spain (2010).
  [ BibTeX ] [ Details ]
   
  @book{AH:10, address = {Barcelona, Spain}, editor = {Atzmueller, Martin and Hotho, Andreas}, publisher = {ECML/PKDD 2010}, title = {Proceedings of the 2010 Workshop on Mining Ubiquitous and Social Environments (MUSE 2010)}, year = 2010 }
• 
  Kluegl, P., Hotho, A., Puppe, F.: Local Adaptive Extraction of References. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., and Schultz, T. (eds.) KI 2010: Advances in Artificial Intelligence, 33rd Annual German Conference on AI. pp. 40-47. Springer (2010).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  The accurate extraction of scholarly reference information from scientific publications is essential for many useful applications like BibTeX management systems or citation analysis. Automatic extraction methods suffer from the heterogeneity of reference notation, no matter wether the extraction model was handcrafted or learnt from labeled data. However, references of the same paper or journal are usually homogeneous. We exploit this local consistency with a novel approach. Given some initial information from such a reference section, we try to derived generalized patterns. These patterns are used to create a local model of the current document. The local model helps to identify errors and to improve the extracted information incrementally during the extraction process. Our approach is implemented with handcrafted transformation rules working on a meta-level being able to correct the information independent of the applied layout style. The experimental results compete very well with the state of the art methods and show an extremely high performance on consistent reference sections.
  @inproceedings{2010-KI-KHP, abstract = {The accurate extraction of scholarly reference information from scientific publications is essential for many useful applications like BibTeX management systems or citation analysis. Automatic extraction methods suffer from the heterogeneity of reference notation, no matter wether the extraction model was handcrafted or learnt from labeled data. However, references of the same paper or journal are usually homogeneous. We exploit this local consistency with a novel approach. Given some initial information from such a reference section, we try to derived generalized patterns. These patterns are used to create a local model of the current document. The local model helps to identify errors and to improve the extracted information incrementally during the extraction process. Our approach is implemented with handcrafted transformation rules working on a meta-level being able to correct the information independent of the applied layout style. The experimental results compete very well with the state of the art methods and show an extremely high performance on consistent reference sections.}, author = {Kluegl, Peter and Hotho, Andreas and Puppe, Frank}, booktitle = {KI 2010: Advances in Artificial Intelligence, 33rd Annual German Conference on AI}, editor = {Dillmann, Rüdiger and Beyerer, Jürgen and Hanebeck, Uwe D. and Schultz, Tanja}, pages = {40-47}, publisher = {Springer}, series = {LNAI 6359}, title = {Local Adaptive Extraction of References}, year = 2010 }
• 
  Benz, D., Hotho, A., Jäschke, R., Stumme, G., Halle, A., Lima, A.G.S., Steenweg, H., Stefani, S.: Academic Publication Management with PUMA - collect, organize and share publications. In: Lalmas, M., Jose, J., Rauber, A., Sebastiani, F., and Frommholz, I. (eds.) Proceedings of the European Conference on Research and Advanced Technology for Digital Libraries (ECDL) 2010. p. 417--420. Springer, Berlin/Heidelberg (2010).
  [ Abstract ] [ BibTeX ] [ Details ]
   
  The PUMA project fosters the Open Access movement und aims at a better support of the researcher’s publication work. PUMA stands for an integrated solution, where the upload of a publication results automatically in an update of both the personal and institutional homepage, the creation of an entry in a social bookmarking systems like BibSonomy, an entry in the academic reporting system of the university, and its publication in the institutional repository. In this poster, we present the main features of our solution.
  @inproceedings{benz2010academic, abstract = {The PUMA project fosters the Open Access movement und aims at a better support of the researcher’s publication work. PUMA stands for an integrated solution, where the upload of a publication results automatically in an update of both the personal and institutional homepage, the creation of an entry in a social bookmarking systems like BibSonomy, an entry in the academic reporting system of the university, and its publication in the institutional repository. In this poster, we present the main features of our solution.}, address = {Berlin/Heidelberg}, author = {Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd and Halle, Axel and Lima, Angela Gerlach Sanches and Steenweg, Helge and Stefani, Sven}, booktitle = {Proceedings of the European Conference on Research and Advanced Technology for Digital Libraries (ECDL) 2010}, editor = {Lalmas, M. and Jose, J. and Rauber, A. and Sebastiani, F. and Frommholz, I.}, pages = {417--420}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Academic Publication Management with PUMA - collect, organize and share publications}, volume = 6273, year = 2010 }
• 
  Benz, D., Hotho, A., Jäschke, R., Stumme, G., Halle, A., Lima-Gerlach, A.S., Steenweg, H., Stefani, S.: Academic Publication Management with PUMA - collect, organize and share publications. Proceedings of the 14. European Conference on Research and Advanced Technology for Digital Libraries. pp. 417-420. (2010).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{Halle:2010, author = {Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd and Halle, Axel and Lima-Gerlach, Angela Sanches and Steenweg, Helge and Stefani, Sven}, booktitle = {Proceedings of the 14. European Conference on Research and Advanced Technology for Digital Libraries}, pages = {417-420.}, title = {Academic Publication Management with PUMA - collect, organize and share publications}, year = 2010 }
• 
  Hotho, A., Ulslev Pedersen, R., Wurst, M.: Ubiquitous Data. Lecture Notes in Computer Science. 61--74 (2010).
  [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  @article{hotho2010ubiquitous, author = {Hotho, Andreas and Ulslev Pedersen, Rasmus and Wurst, Michael}, journal = {Lecture Notes in Computer Science}, number = 6202, pages = {61--74}, publisher = {Springer}, title = {Ubiquitous Data}, year = 2010 }
• 
  Hotho, A., Benz, D., Eisterlehner, F., Jäschke, R., Krause, B., Schmitz, C., Stumme, G.: Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System für Wissenschaftler. HMD -- Praxis der Wirtschaftsinformatik. Heft 271, 47-58 (2010).
  [ Abstract ] [ BibTeX ] [ Details ]
   
  Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer größerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenwärtigkeit, die ständige Verfügbarkeit, aber auch die Möglichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gründe für ihren gegenwärtigen Erfolg. Der Artikel führt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabläufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbezügen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.
  @article{hotho2010publikationsmanagement, abstract = {Kooperative Verschlagwortungs- bzw. Social-Bookmarking-Systeme wie Delicious, Mister Wong oder auch unser eigenes System BibSonomy erfreuen sich immer größerer Beliebtheit und bilden einen zentralen Bestandteil des heutigen Web 2.0. In solchen Systemen erstellen Nutzer leichtgewichtige Begriffssysteme, sogenannte Folksonomies, die die Nutzerdaten strukturieren. Die einfache Bedienbarkeit, die Allgegenwärtigkeit, die ständige Verfügbarkeit, aber auch die Möglichkeit, Gleichgesinnte spontan in solchen Systemen zu entdecken oder sie schlicht als Informationsquelle zu nutzen, sind Gründe für ihren gegenwärtigen Erfolg. Der Artikel führt den Begriff Social Bookmarking ein und diskutiert zentrale Elemente (wie Browsing und Suche) am Beispiel von BibSonomy anhand typischer Arbeitsabläufe eines Wissenschaftlers. Wir beschreiben die Architektur von BibSonomy sowie Wege der Integration und Vernetzung von BibSonomy mit Content-Management-Systemen und Webauftritten. Der Artikel schließt mit Querbezügen zu aktuellen Forschungsfragen im Bereich Social Bookmarking.}, author = {Hotho, Andreas and Benz, Dominik and Eisterlehner, Folke and Jäschke, Robert and Krause, Beate and Schmitz, Christoph and Stumme, Gerd}, journal = {HMD -- Praxis der Wirtschaftsinformatik}, month = {#feb#}, pages = {47-58}, title = {Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System für Wissenschaftler}, volume = {Heft 271}, year = 2010 }
• 
  Atzmueller, M., Benz, D., Hotho, A., Stumme, G. eds: Proceedings of the LWA 2010 - Lernen, Wissen, Adaptivität. Department of Electrical Engineering/Computer Science, Kassel University (2010).
  [ BibTeX ] [ Details ]
   
  @book{aezmueller2010lwa, editor = {Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, publisher = {Department of Electrical Engineering/Computer Science, Kassel University}, series = {Technical report (KIS), 2010-10}, title = {Proceedings of the LWA 2010 - Lernen, Wissen, Adaptivität}, year = 2010 }
• 
  Mitzlaff, F., Benz, D., Stumme, G., Hotho, A.: Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy. Proceedings of the 21st ACM conference on Hypertext and hypermedia. , Toronto, Canada (2010).
  [ BibTeX ] [ Details ]
   
  @inproceedings{eisterlehner2010visit, address = {Toronto, Canada}, author = {Mitzlaff, Folke and Benz, Dominik and Stumme, Gerd and Hotho, Andreas}, booktitle = {Proceedings of the 21st ACM conference on Hypertext and hypermedia}, title = {Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy}, year = 2010 }
• 
  Körner, C., Benz, D., Strohmaier, M., Hotho, A., Stumme, G.: Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity. Proceedings of the 19th International World Wide Web Conference (WWW 2010). ACM, Raleigh, NC, USA (2010).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.
  @inproceedings{koerner2010thinking, abstract = {Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.}, address = {Raleigh, NC, USA}, author = {Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 19th International World Wide Web Conference (WWW 2010)}, month = {apr}, publisher = {ACM}, title = {Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity}, year = 2010 }
• 
  Lerch, H., Krause, B., Hotho, A., Roßnagel, A., Stumme, G.: Social Bookmarking-Systeme – die unerkannten Datensammler - Ungewollte personenbezogene Datenverabeitung? MultiMedia und Recht. 7, 454-458 (2010).
  [ BibTeX ] [ Details ]
   
  @article{lerch2010datenschutz, author = {Lerch, Hana and Krause, Beate and Hotho, Andreas and Roßnagel, Alexander and Stumme, Gerd}, journal = {MultiMedia und Recht}, pages = {454-458}, title = {Social Bookmarking-Systeme – die unerkannten Datensammler - Ungewollte personenbezogene Datenverabeitung?}, volume = 7, year = 2010 }
• 
  Hotho, A.: Data Mining on Folksonomies. In: Armano, G., de Gemmis, M., Semeraro, G., and Vargiu, E. (eds.) Intelligent Information Access. pp. 57-82. Springer, Berlin / Heidelberg (2010).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  Social resource sharing systems are central elements of the Web 2.0 and use all the same kind of lightweight knowledge representation, called folksonomy. As these systems are easy to use, they attract huge masses of users. Data Mining provides methods to analyze data and to learn models which can be used to support users. The application and adaptation of known data mining algorithms to folksonomies with the goal to support the users of such systems and to extract valuable information with a special focus on the Semantic Web is the main target of this paper. In this work we give a short introduction into folksonomies with a focus on our own system BibSonomy. Based on the analysis we made on a large folksonomy dataset, we present the application of data mining algorithms on three different tasks, namely spam detection, ranking and recommendation. To bridge the gap between folksonomies and the Semantic Web, we apply association rule mining to extract relations and present a deeper analysis of statistical measures which can be used to extract tag relations. This approach is complemented by presenting two approaches to extract conceptualizations from folksonomies.
  @incollection{springerlink:10.1007/978-3-642-14000-6_4, abstract = {Social resource sharing systems are central elements of the Web 2.0 and use all the same kind of lightweight knowledge representation, called folksonomy. As these systems are easy to use, they attract huge masses of users. Data Mining provides methods to analyze data and to learn models which can be used to support users. The application and adaptation of known data mining algorithms to folksonomies with the goal to support the users of such systems and to extract valuable information with a special focus on the Semantic Web is the main target of this paper. In this work we give a short introduction into folksonomies with a focus on our own system BibSonomy. Based on the analysis we made on a large folksonomy dataset, we present the application of data mining algorithms on three different tasks, namely spam detection, ranking and recommendation. To bridge the gap between folksonomies and the Semantic Web, we apply association rule mining to extract relations and present a deeper analysis of statistical measures which can be used to extract tag relations. This approach is complemented by presenting two approaches to extract conceptualizations from folksonomies.}, address = {Berlin / Heidelberg}, author = {Hotho, Andreas}, booktitle = {Intelligent Information Access}, editor = {Armano, Giuliano and de Gemmis, Marco and Semeraro, Giovanni and Vargiu, Eloisa}, pages = {57-82}, publisher = {Springer}, series = {Studies in Computational Intelligence}, title = {Data Mining on Folksonomies}, volume = 301, year = 2010 }
• 
  Krause, B., Lerch, H., Hotho, A., Roßnagel, A., Stumme, G.: Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy. Informatik-Spektrum. 1-12 (2010).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
   
  Soziale Tagging-Systeme gehören zu den in den vergangenen Jahren entstandenen Web2.0-Systemen. Sie ermöglichen es Anwendern, beliebige Informationen in das Internet einzustellen und untereinander auszutauschen. Je nach Anbieter verlinken Nutzer Videos, Fotos oder Webseiten und beschreiben die eingestellten Medien mit entsprechenden Schlagwörtern (Tags). Die damit einhergehende freiwillige Preisgabe oftmals persönlicher Informationen wirft Fragen im Bereich der informationellen Selbstbestimmung auf. Dieses Grundrecht gewährleistet dem Einzelnen, grundsätzlich selbst über die Preisgabe und Verwendung seiner persönlichen Daten zu bestimmen. Für viele Funktionalitäten, wie beispielsweise Empfehlungsdienste oder die Bereitstellung einer API, ist eine solche Kontrolle allerdings schwierig zu gestalten. Oftmals existieren keine Richtlinien, inwieweit Dienstanbieter und weitere Dritte diese öffentlichen Daten (und weitere Daten, die bei der Nutzung des Systems anfallen) nutzen dürfen. Dieser Artikel diskutiert anhand eines konkreten Systems typische, für den Datenschutz relevante Funktionalitäten und gibt Handlungsanweisungen für eine datenschutzkonforme technische Gestaltung.
  @article{springerlink:10.1007/s00287-010-0485-8, abstract = {Soziale Tagging-Systeme gehören zu den in den vergangenen Jahren entstandenen Web2.0-Systemen. Sie ermöglichen es Anwendern, beliebige Informationen in das Internet einzustellen und untereinander auszutauschen. Je nach Anbieter verlinken Nutzer Videos, Fotos oder Webseiten und beschreiben die eingestellten Medien mit entsprechenden Schlagwörtern (Tags). Die damit einhergehende freiwillige Preisgabe oftmals persönlicher Informationen wirft Fragen im Bereich der informationellen Selbstbestimmung auf. Dieses Grundrecht gewährleistet dem Einzelnen, grundsätzlich selbst über die Preisgabe und Verwendung seiner persönlichen Daten zu bestimmen. Für viele Funktionalitäten, wie beispielsweise Empfehlungsdienste oder die Bereitstellung einer API, ist eine solche Kontrolle allerdings schwierig zu gestalten. Oftmals existieren keine Richtlinien, inwieweit Dienstanbieter und weitere Dritte diese öffentlichen Daten (und weitere Daten, die bei der Nutzung des Systems anfallen) nutzen dürfen. Dieser Artikel diskutiert anhand eines konkreten Systems typische, für den Datenschutz relevante Funktionalitäten und gibt Handlungsanweisungen für eine datenschutzkonforme technische Gestaltung.}, address = {Berlin / Heidelberg}, author = {Krause, Beate and Lerch, Hana and Hotho, Andreas and Roßnagel, Alexander and Stumme, Gerd}, journal = {Informatik-Spektrum}, pages = {1-12}, publisher = {Springer}, title = {Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy}, year = 2010 }
• 
  Toepfer, M., Kluegl, P., Hotho, A., Puppe., F.: Conditional Random Fields For Local Adaptive Reference Extraction. In: Atzmüller, M., Benz, D., Hotho, A., and Stumme, G. (eds.) Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen & Adaptivitaet. , Kassel, Germany (2010).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  The accurate extraction of bibliographic information from scientific publications is an active field of research. Machine learning and sequence labeling approaches like Conditional Random Fields (CRF) are often applied for this reference extraction task, but still suffer from the ambiguity of reference notation. Reference sections apply a predefined style guide and contain only homogeneous references. Therefore, other references of the same paper or journal often provide evidence how the fields of a reference are correctly labeled. We propose a novel approach that exploits the similarities within a document. Our process model uses information of unlabeled documents directly during the extraction task in order to automatically adapt to the perceived style guide. This is implemented by changing the manifestation of the features for the applied CRF. The experimental results show considerable improvements compared to the common approach. We achieve an average F1 score of 96.7% and an instance accuracy of 85.4% on the test data set.
  @inproceedings{kdml21, abstract = {The accurate extraction of bibliographic information from scientific publications is an active field of research. Machine learning and sequence labeling approaches like Conditional Random Fields (CRF) are often applied for this reference extraction task, but still suffer from the ambiguity of reference notation. Reference sections apply a predefined style guide and contain only homogeneous references. Therefore, other references of the same paper or journal often provide evidence how the fields of a reference are correctly labeled. We propose a novel approach that exploits the similarities within a document. Our process model uses information of unlabeled documents directly during the extraction task in order to automatically adapt to the perceived style guide. This is implemented by changing the manifestation of the features for the applied CRF. The experimental results show considerable improvements compared to the common approach. We achieve an average F1 score of 96.7% and an instance accuracy of 85.4% on the test data set.}, address = {Kassel, Germany}, author = {Toepfer, Martin and Kluegl, Peter and Hotho, Andreas and Puppe., Frank}, booktitle = {Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen & Adaptivitaet}, crossref = {lwa2010}, editor = {Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Conditional Random Fields For Local Adaptive Reference Extraction}, year = 2010 }
• 
  Berendt, B., Hotho, A., Stumme, G.: Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0. Web Semantics: Science, Services and Agents on the World Wide Web. 8, 95 - 96 (2010).
  [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  @article{Berendt201095, author = {Berendt, Bettina and Hotho, Andreas and Stumme, Gerd}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, note = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences}, number = {2-3}, pages = {95 - 96}, title = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0}, volume = 8, year = 2010 }
• 
  Benz, D., Hotho, A., Stumme, G.: Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge. Proceedings of the 2nd Web Science Conference (WebSci10). , Raleigh, NC, USA (2010).
  [ BibTeX ] [ Download ] [ Details ]
   
  @inproceedings{benz2010semantics, address = {Raleigh, NC, USA}, author = {Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 2nd Web Science Conference (WebSci10)}, title = {Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge}, year = 2010 }
• 
  Mitzlaff, F., Atzmüller, M., Benz, D., Hotho, A., Stumme, G.: Community Assessment using Evidence Networks. Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010). , Barcelona, Spain (2010).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.
  @inproceedings{mitzlaff2010community, abstract = {Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes evidence networks using implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented evidence networks using user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well.}, address = {Barcelona, Spain}, author = {Mitzlaff, Folke and Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the Workshop on Mining Ubiquitous and Social Environments (MUSE2010)}, title = {Community Assessment using Evidence Networks}, year = 2010 }

2009 [ nach oben ]

• 
  Benz, D., Krause, B., Kumar, G.P., Hotho, A., Stumme, G.: Characterizing Semantic Relatedness of Search Query Terms. Proceedings of the 1st Workshop on Explorative Analytics of Information Networks (EIN2009). , Bled, Slovenia (2009).
  [ BibTeX ] [ Details ]
   
  @inproceedings{benz2009characterizing, address = {Bled, Slovenia}, author = {Benz, Dominik and Krause, Beate and Kumar, G. Praveen and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 1st Workshop on Explorative Analytics of Information Networks (EIN2009)}, month = {September}, title = {Characterizing Semantic Relatedness of Search Query Terms}, year = 2009 }
• 
  Voss, J., Hotho, A., Jäschke, R.: Mapping Bibliographic Records with Bibliographic Hash Keys. In: Kuhlen, R. (ed.) Information: Droge, Ware oder Commons?. Verlag Werner Hülsbusch (2009).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  This poster presents a set of hash keys for bibliographic records called bibkeys. Unlike other methods of duplicate detection, bibkeys can directly be calculated from a set of basic metadata fields (title, authors/editors, year). It is shown how bibkeys are used to map similar bibliographic records in BibSonomy and among distributed library catalogs and other distributed databases.
  @inproceedings{bibkey2009, abstract = {This poster presents a set of hash keys for bibliographic records called bibkeys. Unlike other methods of duplicate detection, bibkeys can directly be calculated from a set of basic metadata fields (title, authors/editors, year). It is shown how bibkeys are used to map similar bibliographic records in BibSonomy and among distributed library catalogs and other distributed databases.}, author = {Voss, Jakob and Hotho, Andreas and Jäschke, Robert}, booktitle = {Information: Droge, Ware oder Commons?}, editor = {Kuhlen, Rainer}, organization = {Hochschulverband Informationswissenschaft}, publisher = {Verlag Werner Hülsbusch}, series = {Proceedings of the ISI}, title = {Mapping Bibliographic Records with Bibliographic Hash Keys}, year = 2009 }
• 
  Atzmueller, M., Lemmerich, F., Krause, B., Hotho, A.: Who are the Spammers? Understandable Local Patterns for Concept Description. 7th Conference on Computer Methods and Systems. , Krakow, Poland (2009).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{atze09, address = {Krakow, Poland}, author = {Atzmueller, Martin and Lemmerich, Florian and Krause, Beate and Hotho, Andreas}, booktitle = {7th Conference on Computer Methods and Systems}, month = {November}, note = {ISBN 83-916420-5-4}, title = {Who are the Spammers? Understandable Local Patterns for Concept Description}, year = 2009 }
• 
  Markines, B., Cattuto, C., Menczer, F., Benz, D., Hotho, A., Stumme, G.: Evaluating Similarity Measures for Emergent Semantics of Social Tagging. 18th International World Wide Web Conference. p. 641--641 (2009).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We ?nd that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.
  @inproceedings{www200965, abstract = {Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We ?nd that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.}, author = {Markines, Benjamin and Cattuto, Ciro and Menczer, Filippo and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {18th International World Wide Web Conference}, month = {April}, pages = {641--641}, title = {Evaluating Similarity Measures for Emergent Semantics of Social Tagging}, year = 2009 }
• 
  Hotho, A., Jäschke, R., Benz, D., Grahl, M., Krause, B., Schmitz, C., Stumme, G.: Social Bookmarking am Beispiel BibSonomy. In: Blumauer, A. and Pellegrini, T. (eds.) Social Semantic Web. p. 363--391. Springer, Berlin, Heidelberg (2009).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.
  @incollection{hotho2008social, abstract = {BibSonomy ist ein kooperatives Verschlagwortungssystem (Social Bookmarking System), betrieben vom Fachgebiet Wissensverarbeitung der Universität Kassel. Es erlaubt das Speichern und Organisieren von Web-Lesezeichen und Metadaten für wissenschaftlichePublikationen. In diesem Beitrag beschreiben wir die von BibSonomy bereitgestellte Funktionalität, die dahinter stehende Architektursowie das zugrunde liegende Datenmodell. Ferner erläutern wir Anwendungsbeispiele und gehen auf Methoden zur Analyse der in BibSonomy und ähnlichen Systemen enthaltenen Daten ein.}, address = {Berlin, Heidelberg}, author = {Hotho, Andreas and Jäschke, Robert and Benz, Dominik and Grahl, Miranda and Krause, Beate and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Social Semantic Web}, chapter = 18, editor = {Blumauer, Andreas and Pellegrini, Tassilo}, pages = {363--391}, publisher = {Springer}, series = {X.media.press}, title = {Social Bookmarking am Beispiel BibSonomy}, year = 2009 }
• 
  Jäschke, R., Eisterlehner, F., Hotho, A., Stumme, G.: Testing and Evaluating Tag Recommenders in a Live System. In: Benz, D. and Janssen, F. (eds.) Workshop on Knowledge Discovery, Data Mining, and Machine Learning. p. 44 --51 (2009).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstrating the power of the framework.
  @inproceedings{jaeschke2009testingKDML, abstract = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents an evaluation of two exemplarily deployed recommendation methods, demonstrating the power of the framework.}, author = {Jäschke, Robert and Eisterlehner, Folke and Hotho, Andreas and Stumme, Gerd}, booktitle = {Workshop on Knowledge Discovery, Data Mining, and Machine Learning}, editor = {Benz, Dominik and Janssen, Frederik}, month = {sep}, pages = {44 --51}, title = {Testing and Evaluating Tag Recommenders in a Live System}, year = 2009 }
• 
  Atzmueller, M., Lemmerich, F., Krause, B., Hotho, A.: Towards Understanding Spammers - Discovering Local Patterns for Concept Characterization and Description. In: Knobbe, J.F.A. (ed.) Proc. LeGo-09: From Local Patterns to Global Models, Workshop at the 2009 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (2009).
  [ BibTeX ] [ URL ] [ Details ]
   
  @inproceedings{ALKH:09, author = {Atzmueller, Martin and Lemmerich, Florian and Krause, Beate and Hotho, Andreas}, booktitle = {Proc. LeGo-09: From Local Patterns to Global Models, Workshop at the 2009 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases}, editor = {Knobbe, Johannes Fürnkranz Arno}, note = {accepted}, title = {Towards Understanding Spammers - Discovering Local Patterns for Concept Characterization and Description}, year = 2009 }
• 
  Eisterlehner, F., Hotho, A., Jäschke, R. eds: ECML PKDD Discovery Challenge 2009 (DC09). (2009).
  [ BibTeX ] [ URL ] [ Details ]
   
  @proceedings{eisterlehner2009ecmlpkdd, editor = {Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert}, month = {sep}, series = {CEUR-WS.org}, title = {ECML PKDD Discovery Challenge 2009 (DC09)}, volume = 497, year = 2009 }
• 
  Benz, D., Eisterlehner, F., Hotho, A., Jäschke, R., Krause, B., Stumme, G.: Managing publications and bookmarks with BibSonomy. In: Cattuto, C., Ruffo, G., and Menczer, F. (eds.) HT '09: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia. p. 323--324. ACM, New York, NY, USA (2009).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  In this demo we present BibSonomy, a social bookmark and publication sharing system.
  @inproceedings{benz2009managing, abstract = {In this demo we present BibSonomy, a social bookmark and publication sharing system.}, address = {New York, NY, USA}, author = {Benz, Dominik and Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Stumme, Gerd}, booktitle = {HT '09: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia}, editor = {Cattuto, Ciro and Ruffo, Giancarlo and Menczer, Filippo}, month = {jun}, pages = {323--324}, publisher = {ACM}, title = {Managing publications and bookmarks with BibSonomy}, year = 2009 }

2008 [ nach oben ]

• 
  Völker, J., Vrandečić, D., Sure, Y., Hotho, A.: AEON - An approach to the automatic evaluation of ontologies. Applied Ontology. 3, 41--62 (2008).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  OntoClean is an approach towards the formal evaluation of taxonomic relations in ontologies. The application of OntoClean consists of two main steps. First, concepts are tagged according to meta-properties known as rigidity, unity, dependency and identity. Second, the tagged concepts are checked according to predefined constraints to discover taxonomic errors. Although OntoClean is well documented in numerous publications, it is still used rather infrequently due to the high costs of application. Especially, the manual tagging of concepts with the correct meta-properties requires substantial efforts of highly experienced ontology engineers. In order to facilitate the use of OntoClean and to enable the evaluation of real-world ontologies, we provide AEON, a tool which automatically tags concepts with appropriate OntoClean meta-properties and performs the constraint checking. We use the Web as an embodiment of world knowledge, where we search for patterns that indicate how to properly tag concepts. We thoroughly evaluated our approach against a manually created gold standard. The evaluation shows the competitiveness of our approach while at the same time significantly lowering the costs. All of our results, i.e. the tool AEON as well as the experiment data, are publicly available.
  @article{voelker2008aeon, abstract = {OntoClean is an approach towards the formal evaluation of taxonomic relations in ontologies. The application of OntoClean consists of two main steps. First, concepts are tagged according to meta-properties known as rigidity, unity, dependency and identity. Second, the tagged concepts are checked according to predefined constraints to discover taxonomic errors. Although OntoClean is well documented in numerous publications, it is still used rather infrequently due to the high costs of application. Especially, the manual tagging of concepts with the correct meta-properties requires substantial efforts of highly experienced ontology engineers. In order to facilitate the use of OntoClean and to enable the evaluation of real-world ontologies, we provide AEON, a tool which automatically tags concepts with appropriate OntoClean meta-properties and performs the constraint checking. We use the Web as an embodiment of world knowledge, where we search for patterns that indicate how to properly tag concepts. We thoroughly evaluated our approach against a manually created gold standard. The evaluation shows the competitiveness of our approach while at the same time significantly lowering the costs. All of our results, i.e. the tool AEON as well as the experiment data, are publicly available.}, address = {Amsterdam, The Netherlands, The Netherlands}, author = {Völker, Johanna and Vrandečić, Denny and Sure, York and Hotho, Andreas}, journal = {Applied Ontology}, number = {1-2}, pages = {41--62}, publisher = {IOS Press}, title = {AEON - An approach to the automatic evaluation of ontologies}, volume = 3, year = 2008 }
• 
  Hotho, A.: Social Bookmarking. In: Back, A., Gronau, N., and Tochtermann, K. (eds.) Web 2.0 in der Unternehmenspraxis: Grundlagen, Fallstudien und Trends zum Einsatz von Social Software. pp. 26-38. Oldenbourg Verlag, München (2008).
  [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  @inbook{hotho2008bookmarking, address = {München}, author = {Hotho, Andreas}, booktitle = {Web 2.0 in der Unternehmenspraxis: Grundlagen, Fallstudien und Trends zum Einsatz von Social Software}, editor = {Back, Andrea and Gronau, Norbert and Tochtermann, Klaus}, pages = {26-38}, publisher = {Oldenbourg Verlag}, title = {Social Bookmarking}, year = 2008 }
• 
  Berendt, B., Glance, N., Hotho, A. eds: Wikis, Blogs, Bookmarking Tools - Mining the Web 2.0 Workshop. Workshop at 18th Europ. Conf. on Machine Learning (ECML'08) / 11th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'08) (2008).
  [ BibTeX ] [ URL ] [ Details ]
   
  @book{berendt2008challenge, editor = {Berendt, Bettina and Glance, Natalie and Hotho, Andreas}, publisher = {Workshop at 18th Europ. Conf. on Machine Learning (ECML'08) / 11th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'08)}, title = {Wikis, Blogs, Bookmarking Tools - Mining the Web 2.0 Workshop}, year = 2008 }
• 
  Hotho, A., Benz, D., Jäschke, R., Krause, B. eds: ECML PKDD Discovery Challenge 2008 (RSDC'08). Workshop at 18th Europ. Conf. on Machine Learning (ECML'08) / 11th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'08) (2008).
  [ BibTeX ] [ URL ] [ Details ]
   
  @book{hotho2008challenge, editor = {Hotho, Andreas and Benz, Dominik and Jäschke, Robert and Krause, Beate}, publisher = {Workshop at 18th Europ. Conf. on Machine Learning (ECML'08) / 11th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'08)}, title = {ECML PKDD Discovery Challenge 2008 (RSDC'08)}, year = 2008 }
• 
  Jäschke, R., Hotho, A., Schmitz, C., Ganter, B., Stumme, G.: Discovering Shared Conceptualizations in Folksonomies. Web Semantics: Science, Services and Agents on the World Wide Web. 6, 38--53 (2008).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.
  @article{jaeschke2008discovering, abstract = {Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.}, author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd}, booktitle = {Semantic Web and Web 2.0}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, month = {feb}, number = 1, pages = {38--53}, title = {Discovering Shared Conceptualizations in Folksonomies}, volume = 6, year = 2008 }
• 
  Cattuto, C., Benz, D., Hotho, A., Stumme, G.: Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems, http://www.citebase.org/abstract?id=oai:arXiv.org:0805.2045, (2008).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.
  @misc{cattuto-2008, abstract = {Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.}, author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems}, year = 2008 }
• 
  Jäschke, R., Krause, B., Hotho, A., Stumme, G.: Logsonomy — A Search Engine Folksonomy. Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008). AAAI Press (2008).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.
  @inproceedings{Jaeschke2008logsonomy, abstract = {In social bookmarking systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Search engines filter the vast information of the web. Queries describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. The clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. This poster analyzes the topological characteristics of the resulting tripartite hypergraph of queries, users and bookmarks of two query logs and compares it two a snapshot of the folksonomy del.icio.us.}, author = {Jäschke, Robert and Krause, Beate and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the Second International Conference on Weblogs and Social Media(ICWSM 2008)}, publisher = {AAAI Press}, title = {Logsonomy — A Search Engine Folksonomy}, year = 2008 }
• 
  Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag Recommendations in Social Bookmarking Systems. AI Communications. 21, 231-247 (2008).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
   
  Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
  @article{jaeschke2008tag, abstract = {Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.}, address = {Amsterdam}, author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, editor = {Giunchiglia, Enrico}, journal = {AI Communications}, number = 4, pages = {231-247}, publisher = {IOS Press}, title = {Tag Recommendations in Social Bookmarking Systems}, volume = 21, year = 2008 }
• 
  Krause, B., Schmitz, C., Hotho, A., Stumme, G.: The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems. AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web. p. 61--68. ACM, Beijing, China (2008).
  [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
   
  @inproceedings{anti2008krause, address = {New York, NY, USA}, author = {Krause, Beate and Schmitz, Christoph and Hotho, Andreas and Stumme, Gerd}, booktitle = {AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web}, pages = {61--68}, publisher = {ACM}, title = {The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems}, year = 2008 }
• 
  May, M., Berendt, B., Cornuéjols, A., Gama, J., Giannotti, F., Hotho, A., Malerba, D., Menesalvas, E., Morik, K., Pedersen, R., Saitta, L., Saygin, Y., Schuster, A., Vanhoof, K.: Research Challenges in Ubiquitous Knowledge Discovery. Next Generation of Data Mining (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series). Chapman & Hall/CRC (2008).
  [ BibTeX ] [ URL ] [ Details ]
   
  @incollection{1420085867, author = {May, Michael and Berendt, Bettina and Cornuéjols, Antoine and Gama, Jõao and Giannotti, Fosca and Hotho, Andreas and Malerba, Donato and Menesalvas, Ernestina and Morik, Katharina and Pedersen, Rasmus and Saitta, Lorenza and Saygin, Yücel and Schuster, Assaf and Vanhoof, Koen}, booktitle = {Next Generation of Data Mining (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)}, edition = 1, publisher = {Chapman & Hall/CRC}, title = {Research Challenges in Ubiquitous Knowledge Discovery}, year = 2008 }
• 
  Benz, D., Grobelnik, M., Hotho, A., Jäschke, R., Mladenic, D., Servedio, V.D.P., Sizov, S., Szomszor, M.: Analyzing Tag Semantics Across Collaborative Tagging Systems. In: Alani, H., Staab, S., and Stumme, G. (eds.) Social Web Communities. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany (2008).
  [ Abstract ] [ BibTeX ] [ URL ] [ Details ]
   
  The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.
  @inproceedings{benz2008analyzing, abstract = {The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.}, address = {Dagstuhl, Germany}, author = {Benz, Dominik and Grobelnik, Marko and Hotho, Andreas and Jäschke, Robert and Mladenic, Dunja and Servedio, Vito D. P. and Sizov, Sergej and Szomszor, Martin}, booktitle = {Social Web Communities}, editor = {Alani, Harith and Staab, Steffen and Stumme, Gerd}, number = {08391}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik}, series = {Dagstuhl Seminar Proceedings}, title = {Analyzing Tag Semantics Across Collaborative Tagging Systems}, year = 2008 }
• 
  Krause, B., Hotho, A., Stumme, G.: A Comparison of Social Bookmarking with Traditional Search. Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008. pp. 101-113. Springer (2008).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
   
  Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings. Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.
  @inproceedings{krause2008comparison, abstract = {Social bookmarking systems allow users to store links to internet resources on a web page. As social bookmarking systems are growing in popularity, search algorithms have been developed that transfer the idea of link-based rankings in the Web to a social bookmarking system’s data structure. These rankings differ from traditional search engine rankings in that they incorporate the rating of users. In this study, we compare search in social bookmarking systems with traditionalWeb search. In the first part, we compare the user activity and behaviour in both kinds of systems, as well as the overlap of the underlying sets of URLs. In the second part,we compare graph-based and vector space rankings for social bookmarking systems with commercial search engine rankings. Our experiments are performed on data of the social bookmarking system Del.icio.us and on rankings and log data from Google, MSN, and AOL. We will show that part of the difference between the systems is due to different behaviour (e. g., the concatenation of multi-word lexems to single terms in Del.icio.us), and that real-world events may trigger similar behaviour in both kinds of systems. We will also show that a graph-based ranking approach on folksonomies yields results that are closer to the rankings of the commercial search engines than vector space retrieval, and that the correlation is high in particular for the domains that are well covered by the social bookmarking system.}, author = {Krause, Beate and Hotho, Andreas and Stumme, Gerd}, booktitle = {Advances in Information Retrieval, 30th European Conference on IR Research, ECIR 2008}, pages = {101-113}, publisher = {Springer}, title = {A Comparison of Social Bookmarking with Traditional Search}, volume = 4956, year = 2008 }
• 
  Krause, B., Jäschke, R., Hotho, A., Stumme, G.: Logsonomy - social information retrieval with logdata. HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia. p. 157--166. ACM, Pittsburgh, PA, USA (2008).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
   
  Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today's search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user's information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.
  @inproceedings{1379123, abstract = {Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today's search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user's information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.}, address = {New York, NY, USA}, author = {Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd}, booktitle = {HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia}, pages = {157--166}, publisher = {ACM}, title = {Logsonomy - social information retrieval with logdata}, year = 2008 }
• 
  Cattuto, C., Benz, D., Hotho, A., Stumme, G.: Semantic Grounding of Tag Relatedness in Social Bookmarking Systems. The Semantic Web - ISWC 2008. p. 615--631. Springer Berlin / Heidelberg (2008).
  [ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
   
  Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.
  @inproceedings{cattuto2008semantic, abstract = {Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.}, author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {The Semantic Web - ISWC 2008}, pages = {615--631}, publisher = {Springer Berlin / Heidelberg}, series = {Lecture Notes in Computer Science}, title = {Semantic Grounding of Tag Relatedness in Social Bookmarking Systems}, volume = 5318, year = 2008 }

2007 [ nach oben ]

• 
  Hotho, A., Stumme (ed.), G.: Themenheft Web Mining. Künstliche Intelligenz. 5-8 (2007).
  [ BibTeX ] [ URL ] [ Details ]
   
  @article{themenheft2007webmining, author = {Hotho, Andreas and Stumme (ed.), Gerd}, journal = {Künstliche Intelligenz}, number = 3, pages = {5-8}, title = {Themenheft Web Mining}, year = 2007 }
• 
  Grahl, M., Hotho, A., Stumme, G.: Conceptual Clustering of Social Bookmark Sites. In: Hinneburg, A. (ed.) Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007). pp. 50-54. Martin-Luther-Universität Halle-Wittenberg (2007).
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