Flow-based Network Traffic Generation using Generative Adversarial Networks.Ring, Markus; Schlör, Daniel; Landes, Dieter; Hotho, Andreas in CoRR (2018). abs/1810.07795
Flow-based data sets are necessary for evaluating network-based intrusion de- tection systems (NIDS). In this work, we propose a novel methodology for gener- ating realistic flow-based network traffic. Our approach is based on Generative Adversarial Networks (GANs) which achieve good results for image generation. A major challenge lies in the fact that GANs can only process continuous at- tributes. However, flow-based data inevitably contain categorical attributes such as IP addresses or port numbers. Therefore, we propose three different preprocessing approaches for flow-based data in order to transform them into continuous values. Further, we present a new method for evaluating the gener- ated flow-based network traffic which uses domain knowledge to define quality tests. We use the three approaches for generating flow-based network traffic based on the CIDDS-001 data set. Experiments indicate that two of the three approaches are able to generate high quality data.
Burrows Zeta: Varianten und Evaluation.Schöch, Christof; Calvo, José; Zehe, Albin; Hotho, Andreas (2018).
ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings.Hettinger, Lena; Dallmann, Alexander; Zehe, Albin; Niebler, Thomas; Hotho, Andreas (2018).
Adaptive kNN Using Expected Accuracy for Classification of Geo-Spatial Data.Kibanov, Mark; Becker, Martin; Müller, Jürgen; Atzmueller, Martin; Hotho, Andreas; Stumme, Gerd (2018).
Big-Data Helps SDN to Improve Application Specific Quality of Service.Schwarzmann, Susanna; Blenk, Andreas; Dobrijevic, Ognjen; Jarschel, Michael; Hotho, Andreas; Zinner, Thomas; Wamser, Florian in Big Data and Software Defined Networks (2018).
This chapter first provides an outline of the current results in the domains of: (a) quality-of-service (QoS) / quality-of-experience (QoE) control and management (CaM) for real-time multimedia services that is supported by software-defined networking (SDN), and (b) big data analytics and methods that are used for QoS/QoE CaM. Then, three specific use case scenarios with respect to video streaming services are presented, so as to illustrate the expected benefits of incorporating big data analytics into SDN-based CaM for the purposes of improving or optimizing QoS/QoE. In the end, we describe our vision and a high-level view of an SDN-based architecture for QoS/QoE CaM that is enriched with big data analytics' functional blocks and summarize corresponding challenges.
Accessing Information with Tags: Search and Ranking.Navarro Bullock, Beate; Hotho, Andreas; Stumme, Gerd in Social Information Access: Systems and Technologies, P. Brusilovsky, He, D. (eds.) (2018). 310--343.
With the growth of the Social Web, a variety of new web-based services arose and changed the way users interact with the internet and consume information. One central phenomenon was and is tagging which allows to manage, organize and access information in social systems. Tagging helps to manage all kinds of resources, making their access much easier. The first type of social tagging systems were social bookmarking systems, i.e., platforms for storing and sharing bookmarks on the web rather than just in the browser. Meanwhile, (hash-)tagging is central in many other Social Media systems such as social networking sites and micro-blogging platforms. To allow for efficient information access, special algorithms have been developed to guide the user, to search for information and to rank the content based on tagging information contributed by the users.
Detection of slow port scans in flow-based network traffic.Ring, Markus; Landes, Dieter; Hotho, Andreas in PLOS ONE (2018). 13(9) 1-18.
Frequently, port scans are early indicators of more serious attacks. Unfortunately, the detection of slow port scans in company networks is challenging due to the massive amount of network data. This paper proposes an innovative approach for preprocessing flow-based data which is specifically tailored to the detection of slow port scans. The preprocessing chain generates new objects based on flow-based data aggregated over time windows while taking domain knowledge as well as additional knowledge about the network structure into account. The computed objects are used as input for the further analysis. Based on these objects, we propose two different approaches for detection of slow port scans. One approach is unsupervised and uses sequential hypothesis testing whereas the other approach is supervised and uses classification algorithms. We compare both approaches with existing port scan detection algorithms on the flow-based CIDDS-001 data set. Experiments indicate that the proposed approaches achieve better detection rates and exhibit less false alarms than similar algorithms.
EveryAware Gears: A Tool to visualize and analyze all types of Citizen Science Data.Lautenschlager, Florian; Becker, Martin; Steininger, Michael; Hotho, Andreas D. Burghardt, Chen, S., Andrienko, G., Andrienko, N., Purves, R., Diehl, A. (eds.) (2018).
Air Trails -- Urban Air Quality Campaign Exploration Patterns.Becker, Martin; Lautenschlager, Florian; Hotho, Andreas (2018).
Burrows’ Zeta: Exploring and Evaluating Variants and Parameters.Schöch, Christof; Schlör, Daniel; Zehe, Albin; Gebhard, Henning; Becker, Martin; Hotho, Andreas (2018). 274-277.
A White-Box Model for Detecting Author Nationality by Linguistic Differences in Spanish Novels.Zehe, Albin; Schlör, Daniel; Henny-Krahmer, Ulrike; Becker, Martin; Hotho, Andreas (2018).
Analysing Direct Speech in German Novels.Jannidis, Fotis; Konle, Leonard; Zehe, Albin; Hotho, Andreas; Krug, Markus (2018).
Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning.Niebler, Thomas; Becker, Martin; Pölitz, Christian; Hotho, Andreas (2017).
Participatory sensing, opinions and collective awarenessLoreto, Vittorio; Haklay, Mordechai; Hotho, Andreas; Servedio, Vito C. P.; Stumme, Gerd; Theunis, Jan; Tria, Francesca (2017). Springer.
Learning Semantic Relatedness From Human Feedback Using Metric Learning.Niebler, Thomas; Becker, Martin; Pölitz, Christian; Hotho, Andreas (2017).
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.
Mining social semantics on the social web.Hotho, A.; Jaeschke, R.; Lerman, K. in Semantic Web (2017). 8(5) 623--624.
A Bayesian Method for Comparing Hypotheses About Human Trails.Singer, Philipp; Helic, Denis; Hotho, Andreas; Strohmaier, Markus in ACM Trans. Web (2017). 11(3) 14:1--14:29.
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.
MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data.Becker, Martin; Lemmerich, Florian; Singer, Philipp; Strohmaier, Markus; Hotho, Andreas in Data Mining and Knowledge Discovery (2017).
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.
Leveraging User-Interactions for Time-Aware Tag Recommendations.Zoller, Daniel; Doerfel, Stephan; Pölitz, Christian; Hotho, Andreas in CEUR Workshop Proceedings (2017).
Learning Word Embeddings from Tagging Data: A methodological comparison.Niebler, Thomas; Hahn, Luzian; Hotho, Andreas (2017).
A Toolset for Intrusion and Insider Threat Detection.Ring, Markus; Wunderlich, Sarah; Grüdl, Dominik; Landes, Dieter; Hotho, Andreas in Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications, I. Palomares Carrascosa, Kalutarage, H. K., Huang, Y. (eds.) (2017). 3--31.
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.
Sedentary Behavior among National Elite Rowers during Off-Training—A Pilot Study.Sperlich, Billy; Becker, Martin; Hotho, Andreas; Wallmann-Sperlich, Birgit; Sareban, Mahdi; Winkert, Kay; Steinacker, Jürgen M.; Treff, Gunnar in Frontiers in Physiology (2017). 8 655.
The aim of this pilot study was to analyze the off-training physical activity (PA) profile in national elite German U23 rowers during 31 days of their preparation period. The hours spent in each PA category (i.e. sedentary: <1.5 MET; light physical activity: 1.5–3 MET; moderate physical activity: 3–6 MET and vigorous intense physical activity: >6 MET) were calculated for every valid day (i.e. > 480 min of wear time). The off-training PA during 21 weekdays and 10 weekend days of the final 11-wk preparation period was assessed by a wrist-worn multisensory device (Microsoft Band II (MSBII)). A total of 11 rowers provided valid data (i.e. > 480 min/day) for 11.6 week days and 4.8 weekend days during the 31 days observation period. The average sedentary time was 11.63±1.25 hours per day during the week and 12.49±1.10 hours per day on the weekend, with a tendency to be higher on the weekend compared to weekdays (p = 0.06; d = 0.73). The average time in light, moderate and vigorous PA during the weekdays was 1.27±1.15, 0.76±0.37, 0.51±0.44 hours per day and 0.67±0.43, 0.59±0.37, 0.53±0.32 hours per weekend day. Light physical activity was higher during weekdays compared to the weekend (p = 0.04; d = 0.69) Based on our pilot study of eleven national elite rowers we conclude that rowers display a considerable sedentary off-training behavior of more than 11.5 hours/day.
IP2Vec: Learning Similarities Between IP Addresses.Ring, Markus; Landes, Dieter; Dallmann, Alexander; Hotho, Andreas in 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (2017). 657-666.
Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications.Lemmerich, Florian; Singer, Philipp; Becker, Martin; Espin-Noboa, Lisette; Dimitrov, Dimitar; Helic, Denis; Hotho, Andreas; Strohmaier, Markus Y. Altun, Das, K., Mielikäinen, T., Malerba, D., Stefanowski, J., Read, J., vZitnik, M., Ceci, M., Dvzeroski, S. (eds.) (2017). 354--357.
Sequential data can be found in many settings, e.g., as sequences of visited websites or as location sequences of travellers. To improve the understanding of the underlying mechanisms that generate such sequences, the HypTrails approach provides for a novel data analysis method. Based on first-order Markov chain models and Bayesian hypothesis testing, it allows for comparing a set of hypotheses, i.e., beliefs about transitions between states, with respect to their plausibility considering observed data. HypTrails has been successfully employed to study phenomena in the online and the offline world. In this talk, we want to give an introduction to HypTrails and showcase selected real-world applications on urban mobility and reading behavior on Wikipedia.
Flow-based benchmark data sets for intrusion detection.Ring, Markus; Wunderlich, Sarah; Grüdl, Dominik; Landes, Dieter; Hotho, Andreas (2017). 361--369.
Creation of Flow-Based Data Sets for Intrusion Detection.Ring, Markus; Wunderlich, Sarah; Grüdl, Dominik; Landes, Dieter; Hotho, Andreas in Journal of Information Warfare (2017). 16(4) 41-54.
Publicly available labelled data sets are necessary for evaluating anomaly-based Intrusion Detection Systems (IDS). However, existing data sets are often not up-to-date or not yet published because of privacy concerns. This paper identifies requirements for good data sets and proposes an approach for their generation. The key idea is to use a test environment and emulate realistic user behaviour with parameterised scripts on the clients. Comprehensive logging mechanisms provide additional information which may be used for a better understanding of the inner dynamics of an IDS. Finally, the proposed approach is used to generate the flow-based CIDDS-002 data set.
Towards Sentiment Analysis on German Literature.Zehe, Albin; Becker, Martin; Jannidis, Fotis; Hotho, Andreas G. Kern-Isberner, Fürnkranz, J., Thimm, M. (eds.) (2017). 387--394.
Sentiment Analysis is a Natural Language Processing-task that is relevant in a number of contexts, including the analysis of literature. We report on ongoing research towards enabling, for the first time, sentence-level Sentiment Analysis in the domain of German novels. We create a labelled dataset from sentences extracted from German novels and, by adapting existing sentiment classifiers, reach promising F1-scores of 0.67 for binary polarity classification.
Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time.Dallmann, Alexander; Grimm, Alexander; Pölitz, Christian; Zoller, Daniel; Hotho, Andreas (2017).
Recently, Recurrent Neural Networks (RNNs) have been applied to the task of session-based recommendation. These approaches use RNNs to predict the next item in a user session based on the previ- ously visited items. While some approaches consider additional item properties, we argue that item dwell time can be used as an implicit measure of user interest to improve session-based item recommen- dations. We propose an extension to existing RNN approaches that captures user dwell time in addition to the visited items and show that recommendation performance can be improved. Additionally, we investigate the usefulness of a single validation split for model selection in the case of minor improvements and find that in our case the best model is not selected and a fold-like study with different validation sets is necessary to ensure the selection of the best model.
Prediction of Happy Endings in German Novels.Zehe, Albin; Becker, Martin; Hettinger, Lena; Hotho, Andreas; Reger, Isabella; Jannidis, Fotis in CEUR Workshop Proceedings, P. Cellier, Charnois, T., Hotho, A., Matwin, S., Moens, M. -F., Toussaint, Y. (eds.) (2016). (Vol. 1646) 9-16.
Comparison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technologies.Düking, Peter; Hotho, Andreas; Fuss, Franz Konstantin; Holmberg, Hans-Christer; Sperlich, Billy in Frontiers in Physiology (2016). 7(71)
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.
Posted, Visited, Exported: Altmetrics in the Social Tagging System BibSonomy.Zoller, Daniel; Doerfel, Stephan; Jäschke, Robert; Stumme, Gerd; Hotho, Andreas in Journal of Informetrics (2016). 10(3) 732 - 749.
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.
What Users Actually do in a Social Tagging System: A Study of User Behavior in BibSonomy.Doerfel, Stephan; Zoller, Daniel; Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus in ACM Transactions on the Web (2016). 10(2) 14:1--14:32.
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.
Significance Testing for the Classification of Literary Subgenres.Hettinger, Lena; Jannidis, Fotis; Reger, Isabella; Hotho, Andreas (2016).
Classification of Literary Subgenres.Hettinger, Lena; Jannidis, Fotis; Reger, Isabella; Hotho, Andreas (2016).
Extracting Semantics from Random Walks on Wikipedia: Comparing learning and counting methods.Dallmann, Alexander; Niebler, Thomas; Lemmerich, Florian; Hotho, Andreas R. West, Zia, L., Taraborelli, D., Leskovec, J. (eds.) (2016).
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.
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.Cellier, Peggy; Charnois, Thierry; Hotho, Andreas; Matwin, Stan; Moens, Marie-Francine; Toussaint, Yannick in CEUR Workshop Proceedings (2016). (Vol. 1646) CEUR-WS.org.
SparkTrails: A MapReduce Implementation of HypTrails for Comparing Hypotheses About Human Trails.Becker, Martin; Mewes, Hauke; Hotho, Andreas; Dimitrov, Dimitar; Lemmerich, Florian; Strohmaier, Markus J. Bourdeau, Hendler, J., Nkambou, R., Horrocks, I., Zhao, B. Y. (eds.) (2016). 17-18.
Mining Subgroups with Exceptional Transition Behavior.Lemmerich, Florian; Becker, Martin; Singer, Philipp; Helic, Denis; Hotho, Andreas; Strohmaier, Markus B. Krishnapuram, Shah, M., Smola, A. J., Aggarwal, C., Shen, D., Rastogi, R. (eds.) (2016). 965-974.
Extracting Semantics from Unconstrained Navigation on Wikipedia.Niebler, Thomas; Schlör, Daniel; Becker, Martin; Hotho, Andreas in KI (2016). 30(2) 163-168.
Analyzing Features for the Detection of Happy Endings in German Novels.Jannidis, Fotis; Reger, Isabella; Zehe, Albin; Becker, Martin; Hettinger, Lena; Hotho, Andreas (2016).
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.
FolkTrails: Interpreting Navigation Behavior in a Social Tagging System.Niebler, Thomas; Becker, Martin; Zoller, Daniel; Doerfel, Stephan; Hotho, Andreas in CIKM '16 (2016).
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.
Creation of Specific Flow-Based Training Data Sets for Usage Behaviour Classification.Otto, Florian; Ring, Markus; Landes, Dieter; Hotho, Andreas (2016). 437.
ConDist: A Context-Driven Categorical Distance Measure.Ring, Markus; Otto, Florian; Becker, Martin; Niebler, Thomas; Landes, Dieter; Hotho, Andreas ECMLPKDD2015 (ed.) (2015).
On Publication Usage in a Social Bookmarking System.Zoller, Daniel; Doerfel, Stephan; Jäschke, Robert; Stumme, Gerd; Hotho, Andreas (2015).
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.
Media Bias in German Online Newspapers.Dallmann, Alexander; Lemmerich, Florian; Zoller, Daniel; Hotho, Andreas (2015).
Hyptrails: A bayesian approach for comparing hypotheses about human trails.Singer, P.; Helic, D.; Hotho, A.; Strohmaier, M. (2015).
Participatory Patterns in an International Air Quality Monitoring Initiative.Sîrbu, Alina; Becker, Martin; Caminiti, Saverio; De Baets, Bernard; Elen, Bart; Francis, Louise; Gravino, Pietro; Hotho, Andreas; Ingarra, Stefano; Loreto, Vittorio; Molino, Andrea; Mueller, Juergen; Peters, Jan; Ricchiuti, Ferdinando; Saracino, Fabio; Servedio, Vito D. P.; Stumme, Gerd; Theunis, Jan; Tria, Francesca; Van den Bossche, Joris in PLoS ONE (2015). 10(8) e0136763.
<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>
Modeling and Extracting Load Intensity Profiles.v. Kistowski, Jóakim; Nikolas, Herbst.; Zoller, Daniel; Kounev, Samuel; Hotho, Andreas (2015).
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.
Photowalking the city: comparing hypotheses about urban photo trails on Flickr.Becker, Martin; Singer, Philipp; Lemmerich, Florian; Hotho, Andreas; Helic, Denis; Strohmaier, Markus (2015).
Participatory Patterns in an International Air Quality Monitoring Initiative.Sîrbu, Alina; Becker, Martin; Caminiti, Saverio; De Baets, Bernard; Elen, Bart; Francis, Louise; Gravino, Pietro; Hotho, Andreas; Ingarra, Stefano; Loreto, Vittorio; Molino, Andrea; Mueller, Juergen; Peters, Jan; Ricchiuti, Ferdinando; Saracino, Fabio; Servedio, Vito D. P.; Stumme, Gerd; Theunis, Jan; Tria, Francesca; Van den Bossche, Joris (2015).
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.
MicroTrails: Comparing Hypotheses About Task Selection on a Crowdsourcing Platform.Becker, Martin; Borchert, Kathrin; Hirth, Matthias; Mewes, Hauke; Hotho, Andreas; Tran-Gia, Phuoc in i-KNOW '15 (2015). 10:1--10:8.
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.
VizTrails: An Information Visualization Tool for Exploring Geographic Movement Trajectories.Becker, Martin; Singer, Philipp; Lemmerich, Florian; Hotho, Andreas; Helic, Denis; Strohmaier, Markus in HT '15 (2015). 319--320.
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.
Genre classification on German novels.Hettinger, Lena; Becker, Martin; Reger, Isabella; Jannidis, Fotis; Hotho, Andreas (2015).
Automatic Threshold Calculation for the Categorical Distance Measure ConDist.Ring, Markus; Landes, Dieter; Hotho, Andreas in CEUR Workshop Proceedings, R. Bergmann, Görg, S., Müller, G. (eds.) (2015). (Vol. 1458) 52-63.
Of course we share! Testing Assumptions about Social Tagging Systems.Doerfel, Stephan; Zoller, Daniel; Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus (2014).
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.
Ubicon and its Applications for Ubiquitous Social Computing.Atzmueller, Martin; Becker, Martin; Kibanov, Mark; Scholz, Christoph; Doerfel, Stephan; Hotho, Andreas; Macek, Bjoern-Elmar; Mitzlaff, Folke; Mueller, Juergen; Stumme, Gerd in New Review of Hypermedia and Multimedia (2014). 20(1) 53--77.
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.
Evaluating Assumptions about Social Tagging - A Study of User Behavior in BibSonomy.Doerfel, Stephan; Zoller, Daniel; Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus T. Seidl, Hassani, M., Beecks, C. (eds.) (2014). 18--19.
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, 2014Cellier, Peggy; Charnois, Thierry; Hotho, Andreas; Matwin, Stan; Moens, Marie-Francine; Toussaint, Yannick in CEUR Workshop Proceedings (2014). (Vol. 1202) CEUR-WS.org.
How Social is Social Tagging?Doerfel, Stephan; Zoller, Daniel; Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus in WWW 2014 (2014).
The sixth ACM RecSys workshop on recommender systems and the social web.Jannach, Dietmar; Freyne, Jill; Geyer, Werner; Guy, Ido; Hotho, Andreas; Mobasher, Bamshad (2014). 395.
Subjective vs. Objective Data: Bridging the Gap.Becker, Martin; Hotho, Andreas; Mueller, Juergen; Kibanov, Mark; Atzmueller, Martin; Stumme, Gerd (2014).
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.
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, 2014Jannach, Dietmar; Freyne, Jill; Geyer, Werner; Guy, Ido; Hotho, Andreas; Mobasher, Bamshad in CEUR Workshop Proceedings (2014). (Vol. 1271) CEUR-WS.org.
HypTrails: A Bayesian Approach for Comparing Hypotheses about Human Trails on the Web.Singer, Philipp; Helic, Denis; Hotho, Andreas; Strohmaier, Markus (2014).
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.
The social distributional hypothesis: a pragmatic proxy for homophily in online social networks.Mitzlaff, Folke; Atzmueller, Martin; Hotho, Andreas; Stumme, Gerd in Social Network Analysis and Mining (2014). 4(1)
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
Folksonomies.Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus in Encyclopedia of Social Network Analysis and Mining (2014). 542--547.
Informationelle Selbstbestimmung Im Web 2.0 Chancen Und Risiken Sozialer VerschlagwortungssystemeDoerfel, Stephan; Hotho, Andreas; Kartal-Aydemir, Aliye; Roßnagel, Alexander; Stumme, Gerd (2013). Vieweg + Teubner Verlag.
How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems.Niebler, Thomas; Singer, Philipp; Benz, Dominik; Körner, Christian; Strohmaier, Markus; Hotho, Andreas in Advances in Information Retrieval, P. Serdyukov, Braslavski, P., Kuznetsov, S. O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) (2013). (Vol. 7814) 86-97.
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
A Generic Platform for Ubiquitous and Subjective Data.Becker, Martin; Mueller, Juergen; Hotho, Andreas; Stumme, Gerd (2013). New York, NY, USA.
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.
Semantics of User Interaction in Social Media.Mitzlaff, Folke; Atzmueller, Martin; Stumme, Gerd; Hotho, Andreas in Complex Networks IV, G. Ghoshal, Poncela-Casasnovas, J., Tolksdorf, R. (eds.) (2013). (Vol. 476)
Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations.Landia, Nikolas; Doerfel, Stephan; Jäschke, Robert; Anand, Sarabjot Singh; Hotho, Andreas; Griffiths, Nathan in cs.IR (2013). 1310.1498
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.
Awareness and Learning in Participatory Noise Sensing.Becker, Martin; Caminiti, Saverio; Fiorella, Donato; Francis, Louise; Gravino, Pietro; Haklay, Mordechai (Muki); Hotho, Andreas; Loreto, Vittorio; Mueller, Juergen; Ricchiuti, Ferdinando; Servedio, Vito D. P.; Sîrbu, Alina; Tria, Francesca in PLoS ONE (2013). 8(12) e81638.
<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>
Tag Recommendations for SensorFolkSonomies.Mueller, Juergen; Doerfel, Stephan; Becker, Martin; Hotho, Andreas; Stumme, Gerd (2013). (Vol. 1066)
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.
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 PapersAtzmueller, Martin; Chin, Alvin; Helic, Denis; Hotho, Andreas (2013). Imprint: Springer, Berlin, Heidelberg.
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.Mobasher, Bamshad; Jannach, Dietmar; Geyer, Werner; Freyne, Jill; Hotho, Andreas; Anand, Sarabjot Singh; Guy, Ido in CEUR Workshop Proceedings (2013). (Vol. 1066) CEUR-WS.org.
Exploiting Structural Consistencies with Stacked Conditional Random Fields.Kluegl, Peter; Toepfer, Martin; Lemmerich, Florian; Hotho, Andreas; Puppe, Frank in Mathematical Methodologies in Pattern Recognition and Machine Learning Springer Proceedings in Mathematics & Statistics (2013). 30 111-125.
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.
Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia.Singer, Philipp; Niebler, Thomas; Strohmaier, Markus; Hotho, Andreas in International Journal on Semantic Web and Information Systems (IJSWIS) (2013). 9(4) 41--70.
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.
Recommender Systems for Social Tagging SystemsBalby Marinho, L.; Hotho, A.; Jäschke, R.; Nanopoulos, A.; Rendle, S.; Schmidt-Thieme, L.; Stumme, G.; Symeonidis, P. in SpringerBriefs in Electrical and Computer Engineering (2012). Springer.
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.
The challenge of recommender systems challenges.Said, Alan; Tikk, Domonkos; Hotho, Andreas P. Cunningham, Hurley, N. J., Guy, I., Anand, S. S. (eds.) (2012). 9-10.
Stacked Conditional Random Fields Exploiting Structural Consistencies.Klügl, Peter; Toepfer, Martin; Lemmerich, Florian; Hotho, Andreas; Puppe, Frank P. L. Carmona, Sánchez, J. S., Fred, A. (eds.) (2012). 240-248.
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.
RSWeb '12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social webMobasher, Bamshad; Jannach, Dietmar; Geyer, Werner; Hotho, Andreas (2012). ACM, Dublin, Ireland.
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.
Challenges in Tag Recommendations for Collaborative Tagging Systems.Jäschke, Robert; Hotho, Andreas; Mitzlaff, Folke; Stumme, Gerd in Recommender Systems for the Social Web, J. J. Pazos Arias, Fernández Vilas, A., Díaz Redondo, R. P. (eds.) (2012). (Vol. 32) 65--87.
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.
Extending FolkRank with content data.Landia, Nikolas; Anand, Sarabjot Singh; Hotho, Andreas; Jäschke, Robert; Doerfel, Stephan; Mitzlaff, Folke in RSWeb '12 (2012). 1--8.
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.
Leveraging publication metadata and social data into FolkRank for scientific publication recommendation.Doerfel, Stephan; Jäschke, Robert; Hotho, Andreas; Stumme, Gerd in RSWeb '12 (2012). 9--16.
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.
Publikationen im Web 2.0.Hotho, Andreas in Informatik-Spektrum (2012). 1-5.
Ubicon: Observing Social and Physical Activities.Atzmueller, Martin; Becker, Martin; Doerfel, Stephan; Kibanov, Mark; Hotho, Andreas; Macek, Björn-Elmar; Mitzlaff, Folke; Mueller, Juergen; Scholz, Christoph; Stumme, Gerd (2012).
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.
Modeling and Mining Ubiquitous Social MediaAtzmueller, Martin; Chin, Alvin; Helic, Denis; Hotho, Andreas in Lecture Notes in Computer Science (2012). (Vol. 7472) Springer Verlag, Heidelberg, Germany.
4th ACM RecSys workshop on recommender systems and the social web.Mobasher, Bamshad; Jannach, Dietmar; Geyer, Werner; Hotho, Andreas P. Cunningham, Hurley, N. J., Guy, I., Anand, S. S. (eds.) (2012). 345-346.
Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy.Krause, Beate; Lerch, Hana; Hotho, Andreas; Roßnagel, Alexander; Stumme, Gerd in Informatik Spektrum (2012). 35(1) 12-23.
Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles.Atzmueller, Martin; Doerfel, Stephan; Hotho, Andreas; Mitzlaff, Folke; Stumme, Gerd in Modeling and Mining Ubiquitous Social Media (2012). (Vol. 7472)
Proceedings of the Third International Workshop on Mining Ubiquitous and Social Environments (MUSE 2012)Atzmueller, Martin; Hotho, Andreas (2012). Workshop Notes, Bristol, UK.
Collective Information Extraction with Context-Specific Consistencies.Klügl, Peter; Toepfer, Martin; Lemmerich, Florian; Hotho, Andreas; Puppe, Frank in Lecture Notes in Computer Science, P. A. Flach, Bie, T. D., Cristianini, N. (eds.) (2012). (Vol. 7523) 728-743.
Segmentation of References with Skip-Chain Conditional Random Fields for Consistent Label Transitions.Toepfer, Martin; Kluegl, Peter; Hotho, Andreas; Puppe, Frank (2011).
Social Tagging Recommender Systems.Marinho, Leandro Balby; Nanopoulos, Alexandros; Schmidt-Thieme, Lars; Jäschke, Robert; Hotho, Andreas; Stumme, Gerd; Symeonidis, Panagiotis in Recommender Systems Handbook, F. Ricci, Rokach, L., Shapira, B., Kantor, P. B. (eds.) (2011). 615-644.
Resource-Aware On-Line RFID Localization Using Proximity Data.Scholz, Christoph; Doerfel, Stephan; Atzmueller, Martin; Hotho, Andreas; Stumme, Gerd (2011). 129-144.
Combining Data-Driven and Semantic Approaches for Text Mining.Bloehdorn, Stephan; Blohm, Sebastian; Cimiano, Philipp; Giesbrecht, Eugenie; Hotho, Andreas; Lösch, Uta; Mädche, Alexander; Mönch, Eddie; Sorg, Philipp; Staab, Steffen; Völker, Johanna D. Fensel (ed.) (2011). 115-142.
From Semantic Web Mining to Social and Ubiquitous Mining - A Subjective View on Past, Current, and Future Research.Hotho, Andreas; Stumme, Gerd D. Fensel (ed.) (2011). 143-153.
Introduction to the Special Issue on Social Linking and Hypermedia.Cattuto, Ciro; Hotho, Andreas in New Review of Hypermedia and Multimedia (2011). 17(3) 241-242.
3rd workshop on recommender systems and the social web.Freyne, Jill; Anand, Sarabjot Singh; Guy, Ido; Hotho, Andreas in RecSys '11 (2011). 383--384.
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
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 PapersAtzmueller, Martin; Hotho, Andreas; Strohmaier, Markus; Chin, Alvin in Lecture Notes in Computer Science (2011). (Vol. 6904) Springer.
Privacy-aware spam detection in social bookmarking systems.Bullock, Beate Navarro; Lerch, Hana; Rossnagel, Alexander; Hotho, Andreas; Stumme, Gerd in i-KNOW '11 (2011). 15:1--15:8.
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.
Enhancing Social Interactions at Conferences.Atzmueller, Martin; Benz, Dominik; Doerfel, Stephan; Hotho, Andreas; Jäschke, Robert; Macek, Bjoern Elmar; Mitzlaff, Folke; Scholz, Christoph; Stumme, Gerd in it - Information Technology (2011). 53(3) 101--107.
Proceedings of the 2011 International Workshop on Mining Ubiquitous and Social Environments (MUSE 2011)Atzmueller, Martin; Hotho, Andreas (2011). ECML/PKDD 2011, Athens, Greece.
Community Assessment using Evidence Networks.Mitzlaff, Folke; Atzmueller, Martin; Benz, Dominik; Hotho, Andreas; Stumme, Gerd in LNAI (2011). (Vol. 6904)
One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata.Benz, Dominik; Körner, Christian; Hotho, Andreas; Stumme, Gerd; Strohmaier, Markus G. Antoniou, Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., Pan, J., Leenheer, P. D. (eds.) (2011).
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.
Tagging data as implicit feedback for learning-to-rank.Bullock, Beate Navarro; Jäschke, Robert; Hotho, Andreas (2011).
Recommendation in the Social Web.Burke, Robin; Gemmell, Jonathan; Hotho, Andreas; Jäschke, Robert in AI Magazine (2011). 32(3) 46--56.
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.
Resource-Aware On-Line RFID Localization Using Proximity Data.Scholz, Christoph; Doerfel, Stephan; Atzmueller, Martin; Hotho, Andreas; Stumme, Gerd (2011).
Face-to-Face Contacts during LWA 2010 - Communities, Roles, and Key Players.Atzmueller, Martin; Doerfel, Stephan; Hotho, Andreas; Mitzlaff, Folke; Stumme, Gerd (2011).
Towards Mining Semantic Maturity in Social Bookmarking Systems.Atzmueller, Martin; Benz, Dominik; Hotho, Andreas; Stumme, Gerd A. Passant, Fernández, S., Breslin, J., Bojārs, U. (eds.) (2011).
A Comparison of Content-Based Tag Recommendations in Folksonomy Systems.Illig, Jens; Hotho, Andreas; Jäschke, Robert; Stumme, Gerd in Lecture Notes in Computer Science, K. E. Wolff, Palchunov, D. E., Zagoruiko, N. G., Andelfinger, U. (eds.) (2011). (Vol. 6581) 136--149.
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.
Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players.Atzmueller, Martin; Doerfel, Stephan; Hotho, Andreas; Mitzlaff, Folke; Stumme, Gerd (2011).
One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata.Benz, Dominik; Körner, Christian; Hotho, Andreas; Stumme, Gerd; Strohmaier, Markus (2011).
On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission).Mitzlaff, Folke; Atzmueller, Martin; Stumme, Gerd; Hotho, Andreas (2011).
Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy.Krause, Beate; Lerch, Hana; Hotho, Andreas; Roßnagel, Alexander; Stumme, Gerd in Informatik-Spektrum (2010). 1-12.
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.
Proceedings of the LWA 2010 - Lernen, Wissen, AdaptivitätAtzmueller, Martin; Benz, Dominik; Hotho, Andreas; Stumme, Gerd in Technical report (KIS), 2010-10 (2010). Department of Electrical Engineering/Computer Science, Kassel University.
Academic Publication Management with PUMA - collect, organize and share publications.Benz, Dominik; Hotho, Andreas; Jäschke, Robert; Stumme, Gerd; Halle, Axel; Lima-Gerlach, Angela Sanches; Steenweg, Helge; Stefani, Sven (2010). 417-420.
Ubiquitous Data.Hotho, Andreas; Ulslev Pedersen, Rasmus; Wurst, Michael in Lecture Notes in Computer Science (2010). (6202) 61--74.
The Social Bookmark and Publication Management System BibSonomy.Benz, Dominik; Hotho, Andreas; Jäschke, Robert; Krause, Beate; Mitzlaff, Folke; Schmitz, Christoph; Stumme, Gerd in The VLDB Journal (2010). 19(6) 849--875.
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.
Academic Publication Management with PUMA - collect, organize and share publications.Benz, Dominik; Hotho, Andreas; Jäschke, Robert; Stumme, Gerd; Halle, Axel; Lima, Angela Gerlach Sanches; Steenweg, Helge; Stefani, Sven in Lecture Notes in Computer Science, M. Lalmas, Jose, J., Rauber, A., Sebastiani, F., Frommholz, I. (eds.) (2010). (Vol. 6273) 417--420.
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.
Proceedings of the 2010 Workshop on Mining Ubiquitous and Social Environments (MUSE 2010)Atzmueller, Martin; Hotho, Andreas (2010). ECML/PKDD 2010, Barcelona, Spain.
Community Assessment using Evidence Networks.Mitzlaff, Folke; Atzmüller, Martin; Benz, Dominik; Hotho, Andreas; Stumme, Gerd (2010).
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.
Local Adaptive Extraction of References.Kluegl, Peter; Hotho, Andreas; Puppe, Frank in LNAI 6359, R. Dillmann, Beyerer, J., Hanebeck, U. D., Schultz, T. (eds.) (2010). 40-47.
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.
Data Mining on Folksonomies.Hotho, Andreas in Intelligent Information Access, G. Armano, de Gemmis, M., Semeraro, G., Vargiu, E. (eds.) (2010). (Vol. 301) 57-82.
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.
Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy.Mitzlaff, Folke; Benz, Dominik; Stumme, Gerd; Hotho, Andreas (2010).
Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge.Benz, Dominik; Hotho, Andreas; Stumme, Gerd (2010).
Social Bookmarking-Systeme – die unerkannten Datensammler - Ungewollte personenbezogene Datenverabeitung?Lerch, Hana; Krause, Beate; Hotho, Andreas; Roßnagel, Alexander; Stumme, Gerd in MultiMedia und Recht (2010). 7 454-458.
Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0.Berendt, Bettina; Hotho, Andreas; Stumme, Gerd in Web Semantics: Science, Services and Agents on the World Wide Web (2010). 8(2-3) 95 - 96.
Conditional Random Fields For Local Adaptive Reference Extraction.Toepfer, Martin; Kluegl, Peter; Hotho, Andreas; Puppe., Frank M. Atzmüller, Benz, D., Hotho, A., Stumme, G. (eds.) (2010).
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.
Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity.Körner, Christian; Benz, Dominik; Strohmaier, Markus; Hotho, Andreas; Stumme, Gerd (2010).
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.
Query Logs as Folksonomies.Benz, Dominik; Hotho, Andreas; Jäschke, Robert; Krause, Beate; Stumme, Gerd in Datenbank-Spektrum (2010). 10(1) 15--24.
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.
Publikationsmanagement mit BibSonomy -- ein Social-Bookmarking-System für Wissenschaftler.Hotho, Andreas; Benz, Dominik; Eisterlehner, Folke; Jäschke, Robert; Krause, Beate; Schmitz, Christoph; Stumme, Gerd in HMD -- Praxis der Wirtschaftsinformatik (2010). Heft 271 47-58.
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.
Towards Understanding Spammers - Discovering Local Patterns for Concept Characterization and Description.Atzmueller, Martin; Lemmerich, Florian; Krause, Beate; Hotho, Andreas J. F. A. Knobbe (ed.) (2009).
Social Bookmarking am Beispiel BibSonomy.Hotho, Andreas; Jäschke, Robert; Benz, Dominik; Grahl, Miranda; Krause, Beate; Schmitz, Christoph; Stumme, Gerd in Social Semantic Web, A. Blumauer, Pellegrini, T. (eds.) (2009). 363--391.
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.
Mapping Bibliographic Records with Bibliographic Hash Keys.Voss, Jakob; Hotho, Andreas; Jäschke, Robert in Proceedings of the ISI, R. Kuhlen (ed.) (2009).
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.
Evaluating Similarity Measures for Emergent Semantics of Social Tagging.Markines, Benjamin; Cattuto, Ciro; Menczer, Filippo; Benz, Dominik; Hotho, Andreas; Stumme, Gerd (2009). 641--641.
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.
Managing publications and bookmarks with BibSonomy.Benz, Dominik; Eisterlehner, Folke; Hotho, Andreas; Jäschke, Robert; Krause, Beate; Stumme, Gerd C. Cattuto, Ruffo, G., Menczer, F. (eds.) (2009). 323--324.
In this demo we present BibSonomy, a social bookmark and publication sharing system.
ECML PKDD Discovery Challenge 2009 (DC09)Eisterlehner, Folke; Hotho, Andreas; Jäschke, Robert in CEUR-WS.org (2009). (Vol. 497)
Characterizing Semantic Relatedness of Search Query Terms.Benz, Dominik; Krause, Beate; Kumar, G. Praveen; Hotho, Andreas; Stumme, Gerd (2009).
Testing and Evaluating Tag Recommenders in a Live System.Jäschke, Robert; Eisterlehner, Folke; Hotho, Andreas; Stumme, Gerd D. Benz, Janssen, F. (eds.) (2009). 44 --51.
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.
Who are the Spammers? Understandable Local Patterns for Concept Description.Atzmueller, Martin; Lemmerich, Florian; Krause, Beate; Hotho, Andreas (2009).
Analyzing Tag Semantics Across Collaborative Tagging Systems.Benz, Dominik; Grobelnik, Marko; Hotho, Andreas; Jäschke, Robert; Mladenic, Dunja; Servedio, Vito D. P.; Sizov, Sergej; Szomszor, Martin in Dagstuhl Seminar Proceedings, H. Alani, Staab, S., Stumme, G. (eds.) (2008).
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.
Logsonomy — A Search Engine Folksonomy.Jäschke, Robert; Krause, Beate; Hotho, Andreas; Stumme, Gerd (2008).
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.
Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems.Cattuto, Ciro; Benz, Dominik; Hotho, Andreas; Stumme, Gerd (2008).
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.
Discovering Shared Conceptualizations in Folksonomies.Jäschke, Robert; Hotho, Andreas; Schmitz, Christoph; Ganter, Bernhard; Stumme, Gerd in Web Semantics: Science, Services and Agents on the World Wide Web (2008). 6(1) 38--53.
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.
Social Bookmarking.Hotho, Andreas in Web 2.0 in der Unternehmenspraxis: Grundlagen, Fallstudien und Trends zum Einsatz von Social Software, A. Back, Gronau, N., Tochtermann, K. (eds.) (2008). 26-38.
ECML PKDD Discovery Challenge 2008 (RSDC'08)Hotho, Andreas; Benz, Dominik; Jäschke, Robert; Krause, Beate (2008). Workshop at 18th Europ. Conf. on Machine Learning (ECML'08) / 11th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'08).
Wikis, Blogs, Bookmarking Tools - Mining the Web 2.0 WorkshopBerendt, Bettina; Glance, Natalie; Hotho, Andreas (2008). Workshop at 18th Europ. Conf. on Machine Learning (ECML'08) / 11th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'08).
AEON - An approach to the automatic evaluation of ontologies.Völker, Johanna; Vrandečić, Denny; Sure, York; Hotho, Andreas in Applied Ontology (2008). 3(1-2) 41--62.
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.
Tag Recommendations in Social Bookmarking Systems.Jäschke, Robert; Marinho, Leandro; Hotho, Andreas; Schmidt-Thieme, Lars; Stumme, Gerd in AI Communications, (E. Giunchiglia, ed.) (2008). 21(4) 231-247.
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.
Semantic Grounding of Tag Relatedness in Social Bookmarking Systems.Cattuto, Ciro; Benz, Dominik; Hotho, Andreas; Stumme, Gerd in Lecture Notes in Computer Science (2008). (Vol. 5318) 615--631.
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.
Research Challenges in Ubiquitous Knowledge Discovery.May, Michael; Berendt, Bettina; Cornuéjols, Antoine; Gama, Jõao; Giannotti, Fosca; Hotho, Andreas; Malerba, Donato; Menesalvas, Ernestina; Morik, Katharina; Pedersen, Rasmus; Saitta, Lorenza; Saygin, Yücel; Schuster, Assaf; Vanhoof, Koen in Next Generation of Data Mining (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series) (2008). (1st ed.)
The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems.Krause, Beate; Schmitz, Christoph; Hotho, Andreas; Stumme, Gerd (2008). 61--68.
Logsonomy - social information retrieval with logdata.Krause, Beate; Jäschke, Robert; Hotho, Andreas; Stumme, Gerd (2008). 157--166.
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.
A Comparison of Social Bookmarking with Traditional Search.Krause, Beate; Hotho, Andreas; Stumme, Gerd (2008). (Vol. 4956) 101-113.
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.
From Web to Social Web: Discovering and Deploying User and Content ProfilesBerendt, B.; Hotho, A.; Mladenic, D.; Semeraro, G. in LNCS (Vol. 4736) Springer, Berlin, Germany.
This book constitutes the refereed proceedings of the Workshop on Web Mining, WebMine 2006, held in Berlin, Germany, September 18th, 2006. Topics included are data mining based on analysis of bloggers and tagging, web mining, XML mining and further techniques of knowledge discovery. The book is especially valuable for those interested in the aspects of the Social Web (Web 2.0) and its inherent dynamic and diversity of user-generated content.
Themenheft Web Mining.Hotho, Andreas; Stumme (ed.), Gerd in Künstliche Intelligenz (2007). (3) 5-8.
Organizing Publications and Bookmarks in BibSonomy.Jäschke, Robert; Grahl, Miranda; Hotho, Andreas; Krause, Beate; Schmitz, Christoph; Stumme, Gerd H. Alani, Noy, N., Stumme, G., Mika, P., Sure, Y., Vrandecic, D. (eds.) (2007).
Tag Recommendations in Folksonomies.Jäschke, Robert; Marinho, Leandro; Hotho, Andreas; Schmidt-Thieme, Lars; Stumme, Gerd A. Hinneburg (ed.) (2007). 13-20.
Analysis of the Publication Sharing Behaviour in BibSonomy.Jäschke, Robert; Hotho, Andreas; Schmitz, Christoph; Stumme, Gerd in LNCS (2007). (Vol. 4604)
BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.
Learning Disjointness.Völker, Johanna; Vrandecic, Denny; Sure, York; Hotho, Andreas in Lecture Notes in Computer Science, E. Franconi, Kifer, M., May, W. (eds.) (2007). (Vol. 4519)
Tag Recommendations in Folksonomies.Jäschke, Robert; Marinho, Leandro Balby; Hotho, Andreas; Schmidt-Thieme, Lars; Stumme, Gerd in Lecture Notes in Computer Science, J. N. Kok, Koronacki, J., de Mántaras, R. L., Matwin, S., Mladenic, D., Skowron, A. (eds.) (2007). (Vol. 4702) 506-514.
Conceptual Clustering of Social Bookmark Sites.Grahl, Miranda; Hotho, Andreas; Stumme, Gerd A. Hinneburg (ed.) (2007). 50-54.
Position Paper: Ontology Learning from Folksonomies.Benz, Dominik; Hotho, Andreas A. Hinneburg (ed.) 109-112.
Network Properties of Folksonomies.Schmitz, Christoph; Grahl, Miranda; Hotho, Andreas; Stumme, Gerd; Catutto, Ciro; Baldassarri, Andrea; Loreto, Vittorio; Servedio, Vito D. P. (2007).
Conceptual Clustering of Social Bookmarking Sites.Grahl, Miranda; Hotho, Andreas; Stumme, Gerd (2007). 356-364.
Currently, social bookmarking systems provide intuitive support for browsing locally their content. A global view is usually presented by the tag cloud of the system, but it does not allow a conceptual drill-down, e. g., along a conceptual hierarchy. In this paper, we present a clustering approach for computing such a conceptual hierarchy for a given folksonomy. The hierarchy is complemented with ranked lists of users and resources most related to each cluster. The rankings are computed using our FolkRank algorithm. We have evaluated our approach on large scale data from the del.icio.us bookmarking system.
Mining the World Wide Web.Hotho, Andreas; Stumme, Gerd in Künstliche Intelligenz (2007). (3) 5-8.
Network Properties of Folksonomies.Cattuto, Ciro; Schmitz, Christoph; Baldassarri, Andrea; Servedio, Vito D. P.; Loreto, Vittorio; Hotho, Andreas; Grahl, Miranda; Stumme, Gerd in AI Communications (2007). 20(4) 245 - 262.
Proceedings of the First International Workshop on Emergent Semantics and Ontology Evolution, ESOE 2007, co-located with ISWC 2007 + ASWC 2007, Busan, Korea, November 12th, 2007Chen, Liming; Cudré-Mauroux, Philippe; Haase, Peter; Hotho, Andreas; Ong, Ernie in CEUR Workshop Proceedings (Vol. 292) CEUR-WS.org.
Wege zur Entdeckung von Communities in Folksonomies.Jäschke, Robert; Hotho, Andreas; Schmitz, Christoph; Stumme, Gerd S. Braß, Hinneburg, A. (eds.) (2006). 80-84.
Boosting for Text Classification with Semantic Features.Bloehdorn, Stephan; Hotho, Andreas in Advances in Web Mining and Web Usage Analysis (2006). (Vol. 3932) 149--166.
Semantic Network Analysis of Ontologies.Hoser, Bettina; Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph; Stumme, Gerd in LNCS (2006). (Vol. 4011) 514-529.
Learning Ontologies to Improve Text Clustering and Classification.Bloehdorn, Stephan; Cimiano, Philipp; Hotho, Andreas in From Data and Information Analysis to Knowledge Engineering (2006). 334--341.
Recent work has shown improvements in text clustering and classification tasks by integrating conceptual features extracted from ontologies. In this paper we present text mining experiments in the medical domain in which the ontological structures used are acquired automatically in an unsupervised learning process from the text corpus in question. We compare results obtained using the automatically learned ontologies with those obtained using manually engineered ones. Our results show that both types of ontologies improve results on text clustering and classification tasks, whereby the automatically acquired ontologies yield a improvement competitive with the manually engineered ones. ER -
Mining Association Rules in Folksonomies.Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert; Stumme, Gerd in Studies in Classification, Data Analysis, and Knowledge Organization, V. Batagelj, Bock, H. -H., Ferligoj, A., Žiberna, A. (eds.) (2006). 261-270.
Content Aggregation on Knowledge Bases using Graph Clustering.Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert; Stumme, Gerd in LNCS (2006). (Vol. 4011) 530-544.
Emergent Semantics in BibSonomy.Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph; Stumme, Gerd (2006). (Vol. P-94)
Kollaboratives Wissensmanagement.Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert; Stumme, Gerd in Semantic Web - Wege zur vernetzten Wissensgesellschaft, T. Pellegrini, Blumauer, A. (eds.) (2006). 273-290.
Wissensmanagement in zentralisierten Wissensbasen erfordert einen hohen Aufwand für Erstellung und Wartung, und es entspricht nicht immer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen Überblick über zwei aktuelle Ansätze, die durch kollaboratives Wissensmanagement diese Probleme lösen können. Im Peer-to-Peer-Wissensmanagement unterhalten Benutzer dezentrale Wissensbasen, die dann vernetzt werden können, um andere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die Wissensakquisition so einfach wie möglich zu gestalten und so viele Benutzer in den Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.
Semantics, Web and MiningAckermann, Markus; Berendt, Bettina; Grobelnik, Marko; Hotho, Andreas; Mladenic, Dunja; Semeraro, Giovanni; Spiliopoulou, Myra; Stumme, Gerd; Svatek, Vojtech; van Someren, Maarten (2006).
Semantic Web Mining - State of the Art and Future Directions.Stumme, Gerd; Hotho, Andreas; Berendt, Bettina in Journal of Web Semantics (2006). 4(2) 124-143.
SemanticWeb Mining aims at combining the two fast-developing research areas SemanticWeb andWeb Mining. This survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on improving the results ofWeb Mining by exploiting semantic structures in theWeb, and they make use ofWeb Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.
Personalized Information Access in a Bibliographic Peer-to-Peer System.Haase, Peter; Ehrig, Marc; Hotho, Andreas; Schnizler, Björn in Peer-to-Peer and SemanticWeb, Decentralized Management and Exchange of Knowledge and Information, S. Staab, Stuckenschmidt, H. (eds.) (2006). 143--158.
FolkRank: A Ranking Algorithm for Folksonomies.Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph; Stumme, Gerd (2006). 111-114.
In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006.
BibSonomy: A Social Bookmark and Publication Sharing System.Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph; Stumme, Gerd (2006). 87-102.
Workshop on Web Mining 2006 (WebMine)Berendt, B.; Hotho, A.; Mladenic, D.; Semeraro, G. (2006).
Trend Detection in Folksonomies.Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph; Stumme, Gerd in Lecture Notes in Computer Science, Y. S. Avrithis, Kompatsiaris, Y., Staab, S., O'Connor, N. E. (eds.) (Vol. 4306) 56-70.
TRIAS - An Algorithm for Mining Iceberg Tri-Lattices.Jäschke, Robert; Hotho, Andreas; Schmitz, Christoph; Ganter, Bernhard; Stumme, Gerd (2006).
Information Retrieval in Folksonomies: Search and Ranking.Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph; Stumme, Gerd in LNCS (2006). (Vol. 4011) 411-426.
Flow-based Network Traffic Generation using Generative Adversarial Networks. in CoRR (2018). abs/1810.07795
Burrows Zeta: Varianten und Evaluation. (2018).
Big-Data Helps SDN to Improve Application Specific Quality of Service. in Big Data and Software Defined Networks (2018).
Accessing Information with Tags: Search and Ranking. in Social Information Access: Systems and Technologies, P. Brusilovsky, He, D. (eds.) (2018). 310--343.
Participatory sensing, opinions and collective awareness (2017). Springer.
Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time. (2017).
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. in CEUR Workshop Proceedings (2016). (Vol. 1646) CEUR-WS.org.
Informationelle Selbstbestimmung Im Web 2.0 Chancen Und Risiken Sozialer Verschlagwortungssysteme (2013). Vieweg + Teubner Verlag.