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Semantic Web - Publications

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2018

ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings. Hettinger, Lena; Dallmann, Alexander; Zehe, Albin; Niebler, Thomas; Hotho, Andreas (2018).

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@inproceedings{hettinger2018semeval, address = {New Orleans, LA, USA}, author = {Hettinger, Lena and Dallmann, Alexander and Zehe, Albin and Niebler, Thomas and Hotho, Andreas}, booktitle = {Proceedings of International Workshop on Semantic Evaluation (SemEval-2018)}, keywords = {2018 classification embeddings from:schwemmlein myown relation scientific semantic semantic:selected semeval w2v word}, title = {ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings}, year = 2018 }
ClaiRE at SemEval-2018 Task 7 - Extended Version. Hettinger, Lena; Dallmann, Alexander; Zehe, Albin; Niebler, Thomas; Hotho, Andreas (2018).

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In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). Due to space limitations we publish an extended version of Hettinger et al. (2018) including further technical details and changes made to the preprocessing step in the post-evaluation phase. Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75.11% for the first subtask and 81.44% for the second.

@misc{hettinger2018claire, abstract = {In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). Due to space limitations we publish an extended version of Hettinger et al. (2018) including further technical details and changes made to the preprocessing step in the post-evaluation phase. Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75.11% for the first subtask and 81.44% for the second.}, author = {Hettinger, Lena and Dallmann, Alexander and Zehe, Albin and Niebler, Thomas and Hotho, Andreas}, keywords = {extraction from:thoni myown relation scientific semantic semeval tagging}, note = {cite arxiv:1804.05825Comment: This is the extended version for our work: ClaiRE at SemEval-2018 Task 7}, title = {ClaiRE at SemEval-2018 Task 7 - Extended Version}, year = 2018 }

2017

Mining social semantics on the social web. Hotho, A.; Jaeschke, R.; Lerman, K. in Semantic Web (2017). 8(5) 623--624.

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@article{wrro115871, author = {Hotho, A. and Jaeschke, R. and Lerman, K.}, journal = {Semantic Web}, keywords = {2017 mining myown semantic social web from:hotho}, month = {April}, note = {\copyright 2017 IOS Press and the authors. This is an author produced version of a paper subsequently published in Semantic Web. Uploaded in accordance with the publisher's self-archiving policy.}, number = 5, pages = {623--624}, publisher = {IOS Press}, title = {Mining social semantics on the social web}, volume = 8, year = 2017 }
Learning Word Embeddings from Tagging Data: A methodological comparison. Niebler, Thomas; Hahn, Luzian; Hotho, Andreas (2017).

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@inproceedings{niebler2017learning_2, author = {Niebler, Thomas and Hahn, Luzian and Hotho, Andreas}, booktitle = {Proceedings of the LWDA}, keywords = {bibsonomy citeulike delicious embeddings from:thoni glove line myown semantic tag word2vec}, title = {Learning Word Embeddings from Tagging Data: A methodological comparison}, year = 2017 }
Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning. Niebler, Thomas; Becker, Martin; Pölitz, Christian; Hotho, Andreas (2017).

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@inproceedings{niebler2017learning_3, author = {Niebler, Thomas and Becker, Martin and Pölitz, Christian and Hotho, Andreas}, booktitle = {ISWC'17}, keywords = {feedback from:thoni human learning myown posts postsii project:bibsonomy relatedness relative selected semantic semantic:selected semantics}, title = {Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning}, year = 2017 }
Learning Semantic Relatedness From Human Feedback Using Metric Learning. Niebler, Thomas; Becker, Martin; Pölitz, Christian; Hotho, Andreas (2017).

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Assessing the degree of semantic relatedness between words is an important task with a variety of semantic applications, such as ontology learning for the Semantic Web, semantic search or query expansion. To accomplish this in an automated fashion, many relatedness measures have been proposed. However, most of these metrics only encode information contained in the underlying corpus and thus do not directly model human intuition. To solve this, we propose to utilize a metric learning approach to improve existing semantic relatedness measures by learning from additional information, such as explicit human feedback. For this, we argue to use word embeddings instead of traditional high-dimensional vector representations in order to leverage their semantic density and to reduce computational cost. We rigorously test our approach on several domains including tagging data as well as publicly available embeddings based on Wikipedia texts and navigation. Human feedback about semantic relatedness for learning and evaluation is extracted from publicly available datasets such as MEN or WS-353. We find that our method can significantly improve semantic relatedness measures by learning from additional information, such as explicit human feedback. For tagging data, we are the first to generate and study embeddings. Our results are of special interest for ontology and recommendation engineers, but also for any other researchers and practitioners of Semantic Web techniques.

@misc{niebler2017learning, abstract = {Assessing the degree of semantic relatedness between words is an important task with a variety of semantic applications, such as ontology learning for the Semantic Web, semantic search or query expansion. To accomplish this in an automated fashion, many relatedness measures have been proposed. However, most of these metrics only encode information contained in the underlying corpus and thus do not directly model human intuition. To solve this, we propose to utilize a metric learning approach to improve existing semantic relatedness measures by learning from additional information, such as explicit human feedback. For this, we argue to use word embeddings instead of traditional high-dimensional vector representations in order to leverage their semantic density and to reduce computational cost. We rigorously test our approach on several domains including tagging data as well as publicly available embeddings based on Wikipedia texts and navigation. Human feedback about semantic relatedness for learning and evaluation is extracted from publicly available datasets such as MEN or WS-353. We find that our method can significantly improve semantic relatedness measures by learning from additional information, such as explicit human feedback. For tagging data, we are the first to generate and study embeddings. Our results are of special interest for ontology and recommendation engineers, but also for any other researchers and practitioners of Semantic Web techniques.}, author = {Niebler, Thomas and Becker, Martin and Pölitz, Christian and Hotho, Andreas}, keywords = {learning metric myown posts postsii relatedness semantic from:thoni}, note = {cite arxiv:1705.07425}, title = {Learning Semantic Relatedness From Human Feedback Using Metric Learning}, year = 2017 }

2016

FolkTrails: Interpreting Navigation Behavior in a Social Tagging System. Niebler, Thomas; Becker, Martin; Zoller, Daniel; Doerfel, Stephan; Hotho, Andreas in CIKM '16 (2016).

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Social tagging systems have established themselves as a quick and easy way to organize information by annotating resources with tags. In recent work, user behavior in social tagging systems was studied, that is, how users assign tags, and consume content. However, it is still unclear how users make use of the navigation options they are given. Understanding their behavior and differences in behavior of different user groups is an important step towards assessing the effectiveness of a navigational concept and of improving it to better suit the users’ needs. In this work, we investigate navigation trails in the popular scholarly social tagging system BibSonomy from six years of log data. We discuss dynamic browsing behavior of the general user population and show that different navigational subgroups exhibit different navigational traits. Furthermore, we provide strong evidence that the semantic nature of the underlying folksonomy is an essential factor for explaining navigation.

@inproceedings{niebler2016folktrails, abstract = {Social tagging systems have established themselves as a quick and easy way to organize information by annotating resources with tags. In recent work, user behavior in social tagging systems was studied, that is, how users assign tags, and consume content. However, it is still unclear how users make use of the navigation options they are given. Understanding their behavior and differences in behavior of different user groups is an important step towards assessing the effectiveness of a navigational concept and of improving it to better suit the users’ needs. In this work, we investigate navigation trails in the popular scholarly social tagging system BibSonomy from six years of log data. We discuss dynamic browsing behavior of the general user population and show that different navigational subgroups exhibit different navigational traits. Furthermore, we provide strong evidence that the semantic nature of the underlying folksonomy is an essential factor for explaining navigation.}, address = {New York, NY, USA}, author = {Niebler, Thomas and Becker, Martin and Zoller, Daniel and Doerfel, Stephan and Hotho, Andreas}, booktitle = {Proceedings of the 25th ACM International on Conference on Information and Knowledge Management}, keywords = {behavior bibsonomy bibsonomy:selected folktrails from:nosebrain hyptrails myown navigation posts postsII project:bibsonomy semantic social system tagging}, publisher = {ACM}, series = {CIKM '16}, title = {FolkTrails: Interpreting Navigation Behavior in a Social Tagging System}, year = 2016 }
Extracting Semantics from Unconstrained Navigation on Wikipedia. Niebler, Thomas; Schlör, Daniel; Becker, Martin; Hotho, Andreas in KI (2016). 30(2) 163-168.

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@article{journals/ki/NieblerS0H16, author = {Niebler, Thomas and Schlör, Daniel and Becker, Martin and Hotho, Andreas}, journal = {KI}, keywords = {2015 2016 behaviour extracting extraction from:hotho log myown navigation posts postsII relatedness selected semantic semantic:selected user wikipedia}, number = 2, pages = {163-168}, title = {Extracting Semantics from Unconstrained Navigation on Wikipedia}, volume = 30, year = 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. (Hrsg.) (2016).

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Semantic relatedness between words has been extracted from a variety of sources. In this ongoing work, we explore and compare several options for determining if semantic relatedness can be extracted from navigation structures in Wikipedia. In that direction, we first investigate the potential of representation learning techniques such as DeepWalk in comparison to previously applied methods based on counting co-occurrences. Since both methods are based on (random) paths in the network, we also study different approaches to generate paths from Wikipedia link structure. For this task, we do not only consider the link structure of Wikipedia, but also actual navigation behavior of users. Finally, we analyze if semantics can also be extracted from smaller subsets of the Wikipedia link network. As a result we find that representa- tion learning techniques mostly outperform the investigated co-occurrence counting methods on the Wikipedia network. However, we find that this is not the case for paths sampled from human navigation behavior.

@inproceedings{dallmann2016extracting, abstract = {Semantic relatedness between words has been extracted from a variety of sources. In this ongoing work, we explore and compare several options for determining if semantic relatedness can be extracted from navigation structures in Wikipedia. In that direction, we first investigate the potential of representation learning techniques such as DeepWalk in comparison to previously applied methods based on counting co-occurrences. Since both methods are based on (random) paths in the network, we also study different approaches to generate paths from Wikipedia link structure. For this task, we do not only consider the link structure of Wikipedia, but also actual navigation behavior of users. Finally, we analyze if semantics can also be extracted from smaller subsets of the Wikipedia link network. As a result we find that representa- tion learning techniques mostly outperform the investigated co-occurrence counting methods on the Wikipedia network. However, we find that this is not the case for paths sampled from human navigation behavior.}, author = {Dallmann, Alexander and Niebler, Thomas and Lemmerich, Florian and Hotho, Andreas}, booktitle = {Wiki Workshop@ICWSM}, editor = {West, Robert and Zia, Leila and Taraborelli, Dario and Leskovec, Jure}, keywords = {2016 deepwalk deepwiki from:hotho myown navigation posts postsII selected semantic wikipedia word2vec}, title = {Extracting Semantics from Random Walks on Wikipedia: Comparing learning and counting methods}, year = 2016 }

2015

Evaluating Emergent Semantics in Folksonomies on Human Intuition Niebler, Thomas; Becker, Martin; Zoller, Daniel; Doerfel, Stephan; Hotho, Andreas (2015).

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@techreport{tr_niebler2015evaluating, author = {Niebler, Thomas and Becker, Martin and Zoller, Daniel and Doerfel, Stephan and Hotho, Andreas}, keywords = {evaluation from:thoni human intuition myown semantic semantics wordnet}, title = {Evaluating Emergent Semantics in Folksonomies on Human Intuition}, year = 2015 }

2014

Folksonomies. Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus in Encyclopedia of Social Network Analysis and Mining (2014). 542--547.

[ BibTeX ]

@incollection{singer2014folksonomies, author = {Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Encyclopedia of Social Network Analysis and Mining}, keywords = {2014 characterization folksonomy from:hotho myown posts postsi semantic}, pages = {542--547}, publisher = {Springer}, title = {Folksonomies}, year = 2014 }

2013

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. (Hrsg.) (2013). (Bd. 476)

[ BibTeX ] [ DOI ]

@incollection{MASH:13, address = {Heidelberg, Germany}, author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas}, booktitle = {Complex Networks IV}, editor = {Ghoshal, Gourab and Poncela-Casasnovas, Julia and Tolksdorf, Robert}, keywords = {2013 from:hotho media myown semantic social user}, publisher = {Springer Verlag}, series = {Studies in Computational Intelligence}, title = {{Semantics of User Interaction in Social Media}}, volume = 476, year = 2013 }
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. (Hrsg.) (2013). (Bd. 7814) 86-97.

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The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which

@incollection{niebler2013tagging, abstract = {The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which}, author = {Niebler, Thomas and Singer, Philipp and Benz, Dominik and Körner, Christian and Strohmaier, Markus and Hotho, Andreas}, booktitle = {Advances in Information Retrieval}, editor = {Serdyukov, Pavel and Braslavski, Pavel and Kuznetsov, SergeiO. and Kamps, Jaap and Rüger, Stefan and Agichtein, Eugene and Segalovich, Ilya and Yilmaz, Emine}, keywords = {2013 discovery ecir13 from:hotho myown posts postsi pragmtics semantic sense tagging}, pages = {86-97}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, title = {How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems}, volume = 7814, year = 2013 }
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.

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In this article, the authors present a novel approach for computing semantic relatedness and conduct a large-scale study of it on Wikipedia. Unlike existing semantic analysis methods that utilize Wikipedia’s content or link structure, the authors propose to use human navigational paths on Wikipedia for this task. The authors obtain 1.8 million human navigational paths from a semi-controlled navigation experiment – a Wikipedia-based navigation game, in which users are required to find short paths between two articles in a given Wikipedia article network. The authors’ results are intriguing: They suggest that (i) semantic relatedness computed from human navigational paths may be more precise than semantic relatedness computed from Wikipedia’s plain link structure alone and (ii) that not all navigational paths are equally useful. Intelligent selection based on path characteristics can improve accuracy. The authors’ work makes an argument for expanding the existing arsenal of data sources for calculating semantic relatedness and to consider the utility of human navigational paths for this task.

@article{singer2013computing, abstract = {In this article, the authors present a novel approach for computing semantic relatedness and conduct a large-scale study of it on Wikipedia. Unlike existing semantic analysis methods that utilize Wikipedia’s content or link structure, the authors propose to use human navigational paths on Wikipedia for this task. The authors obtain 1.8 million human navigational paths from a semi-controlled navigation experiment – a Wikipedia-based navigation game, in which users are required to find short paths between two articles in a given Wikipedia article network. The authors’ results are intriguing: They suggest that (i) semantic relatedness computed from human navigational paths may be more precise than semantic relatedness computed from Wikipedia’s plain link structure alone and (ii) that not all navigational paths are equally useful. Intelligent selection based on path characteristics can improve accuracy. The authors’ work makes an argument for expanding the existing arsenal of data sources for calculating semantic relatedness and to consider the utility of human navigational paths for this task.}, author = {Singer, Philipp and Niebler, Thomas and Strohmaier, Markus and Hotho, Andreas}, journal = {International Journal on Semantic Web and Information Systems (IJSWIS)}, keywords = {2013 from:hotho myown navigational posts postsi relatedness semantic}, number = 4, pages = {41--70}, publisher = {IGI Global}, title = {Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia}, volume = 9, year = 2013 }
Computing semantic relatedness from human navigational paths on Wikipedia. Singer, Philipp; Niebler, Thomas; Strohmaier, Markus; Hotho, Andreas in WWW '13 Companion, ACM (Hrsg.) (2013). 171--172.

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This paper presents a novel approach for computing semantic relatedness between concepts on Wikipedia by using human navigational paths for this task. Our results suggest that human navigational paths provide a viable source for calculating semantic relatedness between concepts on Wikipedia. We also show that we can improve accuracy by intelligent selection of path corpora based on path characteristics indicating that not all paths are equally useful. Our 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.

@inproceedings{singer2013computing, abstract = {This paper presents a novel approach for computing semantic relatedness between concepts on Wikipedia by using human navigational paths for this task. Our results suggest that human navigational paths provide a viable source for calculating semantic relatedness between concepts on Wikipedia. We also show that we can improve accuracy by intelligent selection of path corpora based on path characteristics indicating that not all paths are equally useful. Our 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.}, address = {Republic and Canton of Geneva, Switzerland}, author = {Singer, Philipp and Niebler, Thomas and Strohmaier, Markus and Hotho, Andreas}, booktitle = {Proceedings of the 22nd international conference on World Wide Web companion}, editor = {ACM}, keywords = {from:thoni human myown navigational paths posts postsi relatedness selected semantic wikipedia}, pages = {171--172}, publisher = {International World Wide Web Conferences Steering Committee}, series = {WWW '13 Companion}, title = {Computing semantic relatedness from human navigational paths on Wikipedia}, year = 2013 }

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. (Hrsg.) (2011).

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@inproceedings{atzmueller2011towards, author = {Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 4th international workshop on Social Data on the Web (SDoW2011)}, editor = {Passant, Alexandre and Fernández, Sergio and Breslin, John and Bojārs, Uldis}, keywords = {2011 bookmarking from:hotho mining myown pattern semantic social}, title = {Towards Mining Semantic Maturity in Social Bookmarking Systems}, year = 2011 }
On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission). Mitzlaff, Folke; Atzmueller, Martin; Stumme, Gerd; Hotho, Andreas (2011).

[ BibTeX ]

@inproceedings{MASH:13b, address = {Bamberg, Germany}, author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas}, booktitle = {Proc. LWA 2013 (KDML Special Track)}, keywords = {2013 from:hotho mining myown semantic social ubiquitous}, publisher = {University of Bamberg}, title = {{On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission)}}, year = 2011 }
From Semantic Web Mining to Social and Ubiquitous Mining - A Subjective View on Past, Current, and Future Research. Hotho, Andreas; Stumme, Gerd D. Fensel (Hrsg.) (2011). 143-153.

[ BibTeX ] [ URL ]

@inproceedings{conf/birthday/HothoS11, author = {Hotho, Andreas and Stumme, Gerd}, booktitle = {Foundations for the Web of Information and Services}, crossref = {conf/birthday/2011studer}, editor = {Fensel, Dieter}, keywords = {2011 from:hotho mining myown semantic social ubiquitous web}, pages = {143-153}, publisher = {Springer}, title = {From Semantic Web Mining to Social and Ubiquitous Mining - A Subjective View on Past, Current, and Future Research.}, year = 2011 }
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 (Hrsg.) (2011). 115-142.

[ BibTeX ] [ URL ]

@inproceedings{conf/birthday/BloehdornBCGHLMMSSV11, author = {Bloehdorn, Stephan and Blohm, Sebastian and Cimiano, Philipp and Giesbrecht, Eugenie and Hotho, Andreas and Lösch, Uta and Mädche, Alexander and Mönch, Eddie and Sorg, Philipp and Staab, Steffen and Völker, Johanna}, booktitle = {Foundations for the Web of Information and Services}, crossref = {conf/birthday/2011studer}, editor = {Fensel, Dieter}, keywords = {2011 data from:hotho mining myown semantic text}, pages = {115-142}, publisher = {Springer}, title = {Combining Data-Driven and Semantic Approaches for Text Mining.}, year = 2011 }

2010

Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity. Körner, Christian; Benz, Dominik; Strohmaier, Markus; Hotho, Andreas; Stumme, Gerd (2010).

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Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.

@inproceedings{koerner2010thinking, abstract = {Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.}, address = {Raleigh, NC, USA}, author = {Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 19th International World Wide Web Conference (WWW 2010)}, keywords = {2010 collaborative from:hotho myown semantic tagging taggingsurvey www www2010}, month = {apr}, publisher = {ACM}, title = {Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity}, year = 2010 }
Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge. Benz, Dominik; Hotho, Andreas; Stumme, Gerd (2010).

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@inproceedings{benz2010semantics, address = {Raleigh, NC, USA}, author = {Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 2nd Web Science Conference (WebSci10)}, keywords = {2010 from:hotho myown ol ontology semantic semantics websci websci10}, title = {Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge}, year = 2010 }
Query Logs as Folksonomies. Benz, Dominik; Hotho, Andreas; Jäschke, Robert; Krause, Beate; Stumme, Gerd in Datenbank-Spektrum (2010). 10(1) 15--24.

[ Abstract ] [ BibTeX ] [ URL ]

Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well.

@article{dominik2010query, abstract = {Query logs provide a valuable resource for preference information in search. A user clicking on a specific resource after submitting a query indicates that the resource has some relevance with respect to the query. To leverage the information ofquery logs, one can relate submitted queries from specific users to their clicked resources and build a tripartite graph ofusers, resources and queries. This graph resembles the folksonomy structure of social bookmarking systems, where users addtags to resources. In this article, we summarize our work on building folksonomies from query log files. The focus is on threecomparative studies of the system’s content, structure and semantics. Our results show that query logs incorporate typicalfolksonomy properties and that approaches to leverage the inherent semantics of folksonomies can be applied to query logsas well.}, author = {Benz, Dominik and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Stumme, Gerd}, journal = {Datenbank-Spektrum}, keywords = {2010 folksonomies from:hotho logs myown query semantic}, month = {#jun#}, number = 1, pages = {15--24}, title = {Query Logs as Folksonomies}, volume = 10, year = 2010 }
Data Mining on Folksonomies. Hotho, Andreas in Intelligent Information Access, G. Armano, de Gemmis, M., Semeraro, G., Vargiu, E. (Hrsg.) (2010). (Bd. 301) 57-82.

[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]

Social resource sharing systems are central elements of the Web 2.0 and use all the same kind of lightweight knowledge representation, called folksonomy. As these systems are easy to use, they attract huge masses of users. Data Mining provides methods to analyze data and to learn models which can be used to support users. The application and adaptation of known data mining algorithms to folksonomies with the goal to support the users of such systems and to extract valuable information with a special focus on the Semantic Web is the main target of this paper. In this work we give a short introduction into folksonomies with a focus on our own system BibSonomy. Based on the analysis we made on a large folksonomy dataset, we present the application of data mining algorithms on three different tasks, namely spam detection, ranking and recommendation. To bridge the gap between folksonomies and the Semantic Web, we apply association rule mining to extract relations and present a deeper analysis of statistical measures which can be used to extract tag relations. This approach is complemented by presenting two approaches to extract conceptualizations from folksonomies.

@incollection{springerlink:10.1007/978-3-642-14000-6_4, abstract = {Social resource sharing systems are central elements of the Web 2.0 and use all the same kind of lightweight knowledge representation, called folksonomy. As these systems are easy to use, they attract huge masses of users. Data Mining provides methods to analyze data and to learn models which can be used to support users. The application and adaptation of known data mining algorithms to folksonomies with the goal to support the users of such systems and to extract valuable information with a special focus on the Semantic Web is the main target of this paper. In this work we give a short introduction into folksonomies with a focus on our own system BibSonomy. Based on the analysis we made on a large folksonomy dataset, we present the application of data mining algorithms on three different tasks, namely spam detection, ranking and recommendation. To bridge the gap between folksonomies and the Semantic Web, we apply association rule mining to extract relations and present a deeper analysis of statistical measures which can be used to extract tag relations. This approach is complemented by presenting two approaches to extract conceptualizations from folksonomies.}, address = {Berlin / Heidelberg}, author = {Hotho, Andreas}, booktitle = {Intelligent Information Access}, editor = {Armano, Giuliano and de Gemmis, Marco and Semeraro, Giovanni and Vargiu, Eloisa}, keywords = {2010 data folksonomies from:hotho mining myown semantic}, pages = {57-82}, publisher = {Springer}, series = {Studies in Computational Intelligence}, title = {Data Mining on Folksonomies}, volume = 301, year = 2010 }
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.

[ BibTeX ] [ URL ] [ DOI ]

@article{Berendt201095, author = {Berendt, Bettina and Hotho, Andreas and Stumme, Gerd}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, keywords = {2010 data from:hotho introduction mining myown network semantic social web}, note = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences}, number = {2-3}, pages = {95 - 96}, title = {Bridging the Gap--Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0}, volume = 8, year = 2010 }

2009

Evaluating Similarity Measures for Emergent Semantics of Social Tagging. Markines, Benjamin; Cattuto, Ciro; Menczer, Filippo; Benz, Dominik; Hotho, Andreas; Stumme, Gerd (2009). 641--641.

[ Abstract ] [ BibTeX ] [ URL ]

Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We ?nd that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.

@inproceedings{www200965, abstract = {Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We ?nd that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.}, author = {Markines, Benjamin and Cattuto, Ciro and Menczer, Filippo and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {18th International World Wide Web Conference}, keywords = {2009 from:hotho measure myown semantic semantics similarity social tagging taggingsurvey tagorapub}, month = {April}, pages = {641--641}, title = {Evaluating Similarity Measures for Emergent Semantics of Social Tagging}, year = 2009 }

2008

Semantic Grounding of Tag Relatedness in Social Bookmarking Systems. Cattuto, Ciro; Benz, Dominik; Hotho, Andreas; Stumme, Gerd in Lecture Notes in Computer Science (2008). (Bd. 5318) 615--631.

[ Abstract ] [ BibTeX ] [ URL ] [ DOI ]

Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.

@inproceedings{cattuto2008semantic, abstract = {Collaborative tagging systems have nowadays become important data sources for populating semantic web applications. For tasks like synonym detection and discovery of concept hierarchies, many researchers introduced measures of tag similarity. Eventhough most of these measures appear very natural, their design often seems to be rather ad hoc, and the underlying assumptionson the notion of similarity are not made explicit. A more systematic characterization and validation of tag similarity interms of formal representations of knowledge is still lacking. Here we address this issue and analyze several measures oftag similarity: Each measure is computed on data from the social bookmarking system del.icio.us and a semantic grounding isprovided by mapping pairs of similar tags in the folksonomy to pairs of synsets in Wordnet, where we use validated measuresof semantic distance to characterize the semantic relation between the mapped tags. This exposes important features of theinvestigated similarity measures and indicates which ones are better suited in the context of a given semantic application.}, author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {The Semantic Web - ISWC 2008}, keywords = {2008 folksonomy from:hotho grounding iswc2008 myown semantic sw tag tagging taggingsurvey webzu}, pages = {615--631}, publisher = {Springer Berlin / Heidelberg}, series = {Lecture Notes in Computer Science}, title = {Semantic Grounding of Tag Relatedness in Social Bookmarking Systems}, volume = 5318, year = 2008 }
Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems. Cattuto, Ciro; Benz, Dominik; Hotho, Andreas; Stumme, Gerd (2008).

[ Abstract ] [ BibTeX ] [ URL ]

Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.

@misc{cattuto-2008, abstract = {Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.}, author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, keywords = {2008 analysis from:hotho learning myown ol ontology semantic similarity tag}, title = {Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems}, year = 2008 }
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.

[ Abstract ] [ BibTeX ] [ URL ] [ Download ]

Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.

@article{jaeschke2008discovering, abstract = {Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.}, author = {Jäschke, Robert and Hotho, Andreas and Schmitz, Christoph and Ganter, Bernhard and Stumme, Gerd}, booktitle = {Semantic Web and Web 2.0}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, keywords = {2008 analysis bibsonomy concept discovering fca folksonomy formal from:hotho myown semantic semantic:selected tagging taggingsurvey}, month = {feb}, number = 1, pages = {38--53}, title = {Discovering Shared Conceptualizations in Folksonomies}, volume = 6, year = 2008 }
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. (Hrsg.) (2008).

[ Abstract ] [ BibTeX ] [ URL ]

The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.

@inproceedings{benz2008analyzing, abstract = {The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance.}, address = {Dagstuhl, Germany}, author = {Benz, Dominik and Grobelnik, Marko and Hotho, Andreas and Jäschke, Robert and Mladenic, Dunja and Servedio, Vito D. P. and Sizov, Sergej and Szomszor, Martin}, booktitle = {Social Web Communities}, editor = {Alani, Harith and Staab, Steffen and Stumme, Gerd}, keywords = {2008 collaborative dagstuhl from:hotho myown semantic tagging taggingsurvey web}, number = {08391}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik}, series = {Dagstuhl Seminar Proceedings}, title = {Analyzing Tag Semantics Across Collaborative Tagging Systems}, year = 2008 }
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.

[ Abstract ] [ BibTeX ] [ URL ]

OntoClean is an approach towards the formal evaluation of taxonomic relations in ontologies. The application of OntoClean consists of two main steps. First, concepts are tagged according to meta-properties known as rigidity, unity, dependency and identity. Second, the tagged concepts are checked according to predefined constraints to discover taxonomic errors. Although OntoClean is well documented in numerous publications, it is still used rather infrequently due to the high costs of application. Especially, the manual tagging of concepts with the correct meta-properties requires substantial efforts of highly experienced ontology engineers. In order to facilitate the use of OntoClean and to enable the evaluation of real-world ontologies, we provide AEON, a tool which automatically tags concepts with appropriate OntoClean meta-properties and performs the constraint checking. We use the Web as an embodiment of world knowledge, where we search for patterns that indicate how to properly tag concepts. We thoroughly evaluated our approach against a manually created gold standard. The evaluation shows the competitiveness of our approach while at the same time significantly lowering the costs. All of our results, i.e. the tool AEON as well as the experiment data, are publicly available.

@article{voelker2008aeon, abstract = {OntoClean is an approach towards the formal evaluation of taxonomic relations in ontologies. The application of OntoClean consists of two main steps. First, concepts are tagged according to meta-properties known as rigidity, unity, dependency and identity. Second, the tagged concepts are checked according to predefined constraints to discover taxonomic errors. Although OntoClean is well documented in numerous publications, it is still used rather infrequently due to the high costs of application. Especially, the manual tagging of concepts with the correct meta-properties requires substantial efforts of highly experienced ontology engineers. In order to facilitate the use of OntoClean and to enable the evaluation of real-world ontologies, we provide AEON, a tool which automatically tags concepts with appropriate OntoClean meta-properties and performs the constraint checking. We use the Web as an embodiment of world knowledge, where we search for patterns that indicate how to properly tag concepts. We thoroughly evaluated our approach against a manually created gold standard. The evaluation shows the competitiveness of our approach while at the same time significantly lowering the costs. All of our results, i.e. the tool AEON as well as the experiment data, are publicly available.}, address = {Amsterdam, The Netherlands, The Netherlands}, author = {Völker, Johanna and Vrandečić, Denny and Sure, York and Hotho, Andreas}, journal = {Applied Ontology}, keywords = {2008 automatic evaluation from:hotho ml myown ontology semantic sw}, number = {1-2}, pages = {41--62}, publisher = {IOS Press}, title = {AEON - An approach to the automatic evaluation of ontologies}, volume = 3, year = 2008 }

2007

Position Paper: Ontology Learning from Folksonomies. Benz, Dominik; Hotho, Andreas A. Hinneburg (Hrsg.) (2007). 109-112.

[ BibTeX ] [ URL ]

@inproceedings{Benz07OL, author = {Benz, Dominik and Hotho, Andreas}, booktitle = {LWA 2007: Lernen - Wissen - Adaption, Halle, September 2007, Workshop Proceedings (LWA)}, crossref = {conf/lwa/2007}, editor = {Hinneburg, Alexander}, keywords = {2007 folksonomy from:hotho kdubiq learning myown ol ontology semantic summerschool webzu}, pages = {109-112}, publisher = {Martin-Luther-University Halle-Wittenberg}, title = {Position Paper: Ontology Learning from Folksonomies.}, year = 2007 }
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.

[ BibTeX ] [ URL ]

@article{cattuto2007network, author = {Cattuto, Ciro and Schmitz, Christoph and Baldassarri, Andrea and Servedio, Vito D. P. and Loreto, Vittorio and Hotho, Andreas and Grahl, Miranda and Stumme, Gerd}, journal = {AI Communications}, keywords = {2007 folksonomies from:hotho kdubiq myown network properties semantic sosbuch summerschool}, number = 4, pages = {245 - 262}, title = {Network Properties of Folksonomies}, volume = 20, year = 2007 }
Network Properties of Folksonomies. Schmitz, Christoph; Grahl, Miranda; Hotho, Andreas; Stumme, Gerd; Catutto, Ciro; Baldassarri, Andrea; Loreto, Vittorio; Servedio, Vito D. P. (2007).

[ BibTeX ]

@inproceedings{schmitz2007network, address = {Banff}, author = {Schmitz, Christoph and Grahl, Miranda and Hotho, Andreas and Stumme, Gerd and Catutto, Ciro and Baldassarri, Andrea and Loreto, Vittorio and Servedio, Vito D. P.}, booktitle = {Proc. WWW2007 Workshop ``Tagging and Metadata for Social Information Organization''}, keywords = {2007 folksonomy from:hotho myown network project:bibsonomy semantic sna workshop}, month = {May}, title = {Network Properties of Folksonomies}, year = 2007 }
Learning Disjointness. Völker, Johanna; Vrandecic, Denny; Sure, York; Hotho, Andreas in Lecture Notes in Computer Science, E. Franconi, Kifer, M., May, W. (Hrsg.) (2007). (Bd. 4519)

[ BibTeX ] [ URL ]

@inproceedings{voelker1:07:eswc, author = {Völker, Johanna and Vrandecic, Denny and Sure, York and Hotho, Andreas}, booktitle = {Proceedings of the European Semantic Web Conference, ESWC2007}, editor = {Franconi, Enrico and Kifer, Michael and May, Wolfgang}, keywords = {2007 eswc from:hotho learning myown ol ontology semantic}, month = {July}, publisher = {Springer-Verlag}, series = {Lecture Notes in Computer Science}, title = {{Learning Disjointness}}, volume = 4519, year = 2007 }

2006

Semantics, Web and Mining Ackermann, Markus; Berendt, Bettina; Grobelnik, Marko; Hotho, Andreas; Mladenic, Dunja; Semeraro, Giovanni; Spiliopoulou, Myra; Stumme, Gerd; Svatek, Vojtech; van Someren, Maarten (2006).

[ BibTeX ] [ URL ]

@book{Semantic2006Ackermann, author = {Ackermann, Markus and Berendt, Bettina and Grobelnik, Marko and Hotho, Andreas and Mladenic, Dunja and Semeraro, Giovanni and Spiliopoulou, Myra and Stumme, Gerd and Svatek, Vojtech and van Someren, Maarten}, keywords = {2006 from:hotho mining myown semantic web}, title = {Semantics, Web and Mining}, year = 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.

[ Abstract ] [ BibTeX ] [ URL ]

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.

@article{jws2006Semantic, abstract = {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.}, author = {Stumme, Gerd and Hotho, Andreas and Berendt, Bettina}, journal = {Journal of Web Semantics}, keywords = {2006 SumSchool06 from:hotho mining myown semantic web}, number = 2, pages = {124-143}, publisher = {Elsevier}, title = {Semantic Web Mining - State of the Art and Future Directions}, volume = 4, year = 2006 }
Semantic Network Analysis of Ontologies. Hoser, Bettina; Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph; Stumme, Gerd in LNCS (2006). (Bd. 4011) 514-529.

[ BibTeX ] [ URL ]

@inproceedings{hoser2006semantic, address = {Budva, Montenegro}, author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Proceedings of the 3rd European Semantic Web Conference}, keywords = {2006 analysis from:hotho myown network ontology semantic sna social web}, month = {June}, pages = {514-529}, publisher = {Springer}, series = {LNCS}, title = {Semantic Network Analysis of Ontologies}, volume = 4011, year = 2006 }
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. (Hrsg.) (2006). 261-270.

[ BibTeX ] [ URL ] [ DOI ]

@inproceedings{schmitz2006mining, address = {Berlin/Heidelberg}, author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, booktitle = {Data Science and Classification (Proc. IFCS 2006 Conference)}, editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and Žiberna, A.}, keywords = {2006 analysis association folksonomy from:hotho kdubiq myown network rules semantic seminar2006 sosbuch summerschool}, month = {July}, note = {Ljubljana}, pages = {261-270}, publisher = {Springer}, series = {Studies in Classification, Data Analysis, and Knowledge Organization}, title = {Mining Association Rules in Folksonomies}, year = 2006 }
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.

[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ]

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 -

@incollection{bloehdorn2006learning, abstract = {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 -}, author = {Bloehdorn, Stephan and Cimiano, Philipp and Hotho, Andreas}, booktitle = {From Data and Information Analysis to Knowledge Engineering}, keywords = {2006 classification clustering from:hotho myown ol semantic text}, pages = {334--341}, publisher = {Springer Berlin Heidelberg}, title = {Learning Ontologies to Improve Text Clustering and Classification}, year = 2006 }
Kollaboratives Wissensmanagement. Schmitz, Christoph; Hotho, Andreas; Jäschke, Robert; Stumme, Gerd in Semantic Web - Wege zur vernetzten Wissensgesellschaft, T. Pellegrini, Blumauer, A. (Hrsg.) (2006). 273-290.

[ Abstract ] [ BibTeX ] [ URL ]

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.

@incollection{schmitz2006kollaboratives, abstract = {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.}, author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, booktitle = {Semantic Web - Wege zur vernetzten Wissensgesellschaft}, editor = {Pellegrini, Tassilo and Blumauer, Andreas}, keywords = {2006 Wissensmanagement collaborative folksonomy from:hotho knowledge management myown semantic web}, pages = {273-290}, publisher = {Springer}, title = {Kollaboratives Wissensmanagement}, year = 2006 }
Information Retrieval in Folksonomies: Search and Ranking. Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph; Stumme, Gerd in LNCS (2006). (Bd. 4011) 411-426.

[ BibTeX ] [ URL ] [ Download ]

@inproceedings{hotho2006information, address = {Budva, Montenegro}, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Proceedings of the 3rd European Semantic Web Conference}, keywords = {2006 bibsonomy bibsonomy:selected definition folkrank folksonomy from:hotho ir kdubiq myown pagerank project:bibsonomy rank ranking search semantic seminar2006 sosbuch summerschool}, month = {June}, pages = {411-426}, publisher = {Springer}, series = {LNCS}, title = {Information Retrieval in Folksonomies: Search and Ranking}, volume = 4011, year = 2006 }
FolkRank: A Ranking Algorithm for Folksonomies. Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph; Stumme, Gerd (2006). 111-114.

[ Abstract ] [ BibTeX ] [ URL ]

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.

@inproceedings{hotho2006folkrank, abstract = {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.}, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Proc. FGIR 2006}, keywords = {2006 folkrank folksonomy from:hotho myown ranking semantic}, pages = {111-114}, title = {FolkRank: A Ranking Algorithm for Folksonomies}, year = 2006 }
Emergent Semantics in BibSonomy. Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph; Stumme, Gerd (2006). (Bd. P-94)

[ BibTeX ] [ URL ]

@inproceedings{hotho2006emergent, address = {Dresden}, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {Proc. Workshop on Applications of Semantic Technologies, Informatik 2006}, keywords = {2006 bibsonomy emergence from:hotho myown project:bibsonomy semantic semantics}, month = {oct}, title = {Emergent Semantics in BibSonomy}, volume = {P-94}, year = 2006 }
Boosting for Text Classification with Semantic Features. Bloehdorn, Stephan; Hotho, Andreas in Advances in Web Mining and Web Usage Analysis (2006). (Bd. 3932) 149--166.

[ BibTeX ] [ URL ] [ DOI ]

@incollection{bloehdorn2006boosting, author = {Bloehdorn, Stephan and Hotho, Andreas}, booktitle = {Advances in Web Mining and Web Usage Analysis}, keywords = {2006 boosting classification from:hotho myown semantic text}, pages = {149--166}, publisher = {Springer}, series = {LNCS}, title = {Boosting for Text Classification with Semantic Features}, volume = 3932, year = 2006 }

2005

Semantic Web Mining and the Representation, Analysis, and Evolution of Web Space. Berendt, Bettina; Hotho, Andreas; Stumme, Gerd V. Svatek, Snasel, V. (Hrsg.) (2005). 1--16.

[ BibTeX ] [ URL ]

@inproceedings{berendt05semantic, author = {Berendt, Bettina and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proc. of the 1st Intl. Workshop on Representation and Analysis of Web Space}, editor = {Svatek, Vojtech and Snasel, Vaclav}, keywords = {2005 SumSchool06 from:hotho mining myown semantic web}, pages = {1--16}, publisher = {Technical University of Ostrava}, title = {Semantic Web Mining and the Representation, Analysis, and Evolution of Web Space}, year = 2005 }
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis. Cimiano, Philipp; Hotho, Andreas; Staab, Steffen in Journal on Artificial Intelligence Research (2005). 24 305-339.

[ BibTeX ] [ URL ] [ Download ]

@article{cimiano05learning, author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen}, journal = {Journal on Artificial Intelligence Research}, keywords = {2005 fca folksonomy from:hotho hierarchies hierarchy learning myown ontologies semantic text}, pages = {305-339}, title = {Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis}, volume = 24, year = 2005 }
An Ontology-based Framework for Text Mining. Bloehdorn, Stephan; Cimiano, Philipp; Hotho, Andreas; Staab, Steffen in LDV Forum - GLDV Journal for Computational Linguistics and Language Technology (2005). 20(1) 87-112.

[ BibTeX ]

@article{bloehdorn-etal-ldv-2005, author = {Bloehdorn, Stephan and Cimiano, Philipp and Hotho, Andreas and Staab, Steffen}, journal = {LDV Forum - GLDV Journal for Computational Linguistics and Language Technology}, keywords = {2005 SumSchool06 framework from:hotho mining myown ontology semantic text}, month = {MAY}, number = 1, pages = {87-112}, title = {An Ontology-based Framework for Text Mining}, volume = 20, year = 2005 }

2004

Web Mining: From Web to Semantic Web Berendt, Bettina; Hotho, Andreas; Mladenic, Dunja; van Someren, Maarten; Spiliopoulou, Myra; Stumme, Gerd in LNAI (2004). (Bd. 3209) Springer, Heidelberg.

[ BibTeX ]

@book{Berendt2004Web, address = {Heidelberg}, editor = {Berendt, Bettina and Hotho, Andreas and Mladenic, Dunja and van Someren, Maarten and Spiliopoulou, Myra and Stumme, Gerd}, keywords = {2004 from:hotho mining myown semantic web}, publisher = {Springer}, series = {LNAI}, title = {Web Mining: From Web to Semantic Web}, volume = 3209, year = 2004 }
Usage Mining for and on the Semantic Web. Berendt, B.; Hotho, A.; Stumme, G. in Data Mining Next Generation Challenges and Future Directions, H. Kargupta, Joshi, A., Sivakumar, K., Yesha, Y. (Hrsg.) (2004). 461-481.

[ BibTeX ]

@incollection{berendt03usage, address = {Boston}, author = {Berendt, B. and Hotho, A. and Stumme, G.}, booktitle = {Data Mining Next Generation Challenges and Future Directions}, editor = {Kargupta, Hillol and Joshi, Anupam and Sivakumar, Krishnamoorthy and Yesha, Yelena}, keywords = {2003 from:hotho myown semantic web wum}, pages = {461-481}, publisher = {AAAI Press}, title = {Usage Mining for and on the Semantic Web.}, year = 2004 }
Semantic Web Personalization Mobasher, Bamshad; Anand, Sarabjot Singh; Berendt, Bettina; Hotho, Andreas (2004).

[ BibTeX ]

@proceedings{semwebpers2004, editor = {Mobasher, Bamshad and Anand, Sarabjot Singh and Berendt, Bettina and Hotho, Andreas}, keywords = {2004 from:hotho myown personalization semantic web}, month = {SEP}, note = {\url{http://maya.cs.depaul.edu/~mobasher/swp04/}}, title = {Semantic Web Personalization}, year = 2004 }
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis Cimiano, Philipp; Hotho, Andreas; Staab, Steffen (2004).

[ BibTeX ] [ URL ]

@techreport{cimianpo_hotho_staab_OL_FCA_04, author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen}, institution = {Institute AIFB, Universität Karlsruhe}, keywords = {2004 fca from:hotho hierarchies learning myown semantic}, month = {NOV}, title = {Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis}, year = 2004 }
International Workshop on Mining for and from the Semantic Web (MSW2004) Hotho, Andreas; Sure, York; Getoor, Lise (2004).

[ BibTeX ] [ URL ]

@proceedings{msw2004, editor = {Hotho, Andreas and Sure, York and Getoor, Lise}, keywords = {2004 from:hotho mining myown semantic web workshop}, month = {AUG}, note = {located at the 10th International ACM SIGKDD Conference on Knowledge Discovery and Data Mining KDD 2004, 22nd August 2004, Seattle, WA, USA}, title = {International Workshop on Mining for and from the Semantic Web (MSW2004)}, year = 2004 }
Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies. Cimiano, Philipp; Hotho, Andreas; Stumme, Gerd; Tane, Julien in LNCS (2004). (Bd. 2961)

[ BibTeX ] [ URL ]

@inproceedings{cim04b, author = {Cimiano, Philipp and Hotho, Andreas and Stumme, Gerd and Tane, Julien}, booktitle = {Proceedings of the The Second International Conference on Formal Concept Analysis (ICFCA 04)}, keywords = {2004 fca from:hotho myown ol ontology semantic}, publisher = {Springer}, series = {LNCS}, title = {Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies}, volume = 2961, year = 2004 }
Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text. Cimiano, Philipp; Hotho, Andreas; Staab, Steffen R. L. de Mántaras, Saitta, L. (Hrsg.) (2004). 435-439.

[ BibTeX ] [ URL ]

@inproceedings{cim04c, address = {Valencia, Spain}, author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen}, booktitle = {Proceedings of the European Conference on Artificial Intelligence (ECAI'04)}, editor = {de M{á}ntaras, Ramon L{ó}pez and Saitta, Lorenza}, keywords = {2004 clustering from:hotho learning myown semantic taxonomies}, pages = {435-439}, publisher = {IOS Press}, title = {Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text}, year = 2004 }
Clustering Ontologies from Text. Cimiano, Philipp; Hotho, Andreas; Staab, Steffen (2004).

[ BibTeX ] [ URL ]

@inproceedings{cim04a, address = {Lisbon, Portugal}, author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen}, booktitle = {Proceedings of the Conference on Languages Resources and Evaluation (LREC)}, keywords = {2004 clustering from:hotho myown ol ontology semantic text}, month = {MAY}, publisher = {ELRA - European Language Ressources Association}, title = {Clustering Ontologies from Text}, year = 2004 }
Boosting for Text Classification with Semantic Features. Bloehdorn, Stephan; Hotho, Andreas (2004). 70-87.

[ BibTeX ] [ URL ]

@inproceedings{bloehdorn04msw, author = {Bloehdorn, Stephan and Hotho, Andreas}, booktitle = {Proceedings of the MSW 2004 workshop at the 10th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, keywords = {2004 SumSchool06 boosting classification from:hotho myown ontology semantic text}, month = {AUG}, pages = {70-87}, title = {Boosting for Text Classification with Semantic Features}, year = 2004 }
A workshop report: mining for and from the Semantic Web at KDD 2004. Hotho, Andreas; Sure, York; Getoor, Lise in SIGKDD Explorations (2004). 6(2) 142-143.

[ BibTeX ]

@article{DBLP:journals/sigkdd/HothoSG04, author = {Hotho, Andreas and Sure, York and Getoor, Lise}, journal = {SIGKDD Explorations}, keywords = {2004 from:hotho mining myown report semantic web workshop}, number = 2, pages = {142-143}, title = {A workshop report: mining for and from the Semantic Web at KDD 2004.}, volume = 6, year = 2004 }
A roadmap for web mining: From web to semantic web. Berendt, Bettina; Hotho, Andreas; Mladenic, Dunja; Van Someren, Maarten; Spiliopoulou, Myra; Stumme, Gerd in Web Mining: From Web to Semantic Web (2004). 1--22.

[ BibTeX ] [ Download ]

@incollection{berendt2004roadmap, author = {Berendt, Bettina and Hotho, Andreas and Mladenic, Dunja and Van Someren, Maarten and Spiliopoulou, Myra and Stumme, Gerd}, booktitle = {Web Mining: From Web to Semantic Web}, keywords = {2004 citedby:scholar:count:80 citedby:scholar:timestamp:2018-2-13 from:hotho mining myown semantic web}, pages = {1--22}, publisher = {Springer}, title = {A roadmap for web mining: From web to semantic web}, year = 2004 }

2003

Semantic Web - State of the art and future directions. Studer, Rudi; Volz, Raphael; Stumme, Gerd; Hotho, Andreas in KI Heft, Special Issue on the Semantic Web (2003). 3 5-9.

[ BibTeX ]

@article{studer03semanticweb, author = {Studer, Rudi and Volz, Raphael and Stumme, Gerd and Hotho, Andreas}, journal = {KI Heft, Special Issue on the Semantic Web}, keywords = {2003 from:hotho future myown semantic web}, pages = {5-9}, title = {Semantic Web - State of the art and future directions}, volume = 3, year = 2003 }
Ontology-based Text Document Clustering. Staab, Steffen; Hotho, Andreas (2003). 451-452.

[ BibTeX ] [ URL ]

@inproceedings{conf/iis/StaabH03, author = {Staab, Steffen and Hotho, Andreas}, booktitle = {Intelligent Information Processing and Web Mining, Proceedings of the International IIS: IIPWM'03 Conference held in Zakopane}, keywords = {2003 clustering from:hotho myown ontology semantic text}, pages = {451-452}, title = {Ontology-based Text Document Clustering.}, year = 2003 }
Ontologies Improve Text Document Clustering. Hotho, A.; Staab, S.; Stumme, G. (2003). 541-544.

[ BibTeX ] [ URL ]

@inproceedings{hotho_icdm03, author = {Hotho, A. and Staab, S. and Stumme, G.}, booktitle = {Proc. of the ICDM 03, The 2003 IEEE International Conference on Data Mining}, keywords = {2003 clustering from:hotho myown ontology semantic semantic:selected text}, pages = {541-544}, title = {Ontologies Improve Text Document Clustering}, year = 2003 }
Explaining Text Clustering Results using Semantic Structures. Hotho, A.; Staab, S.; Stumme, G. in LNCS (2003). (Bd. 2838) 217-228.

[ BibTeX ]

@inproceedings{hotho_pkdd03, author = {Hotho, A. and Staab, S. and Stumme, G.}, booktitle = {Proc. of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD}, keywords = {2003 SumSchool06 clustering fca from:hotho myown semantic text visualization}, pages = {217-228}, series = {LNCS}, title = {Explaining Text Clustering Results using Semantic Structures}, volume = 2838, year = 2003 }
Building and Using the Semantic Web. Studer, Rudi; Stumme, Gerd; Handschuh, Siegfried; Hotho, Andreas; Motik, Boris (2003). 31-34.

[ BibTeX ]

@inproceedings{studer03Building, address = {Osaka, Japan}, author = {Studer, Rudi and Stumme, Gerd and Handschuh, Siegfried and Hotho, Andreas and Motik, Boris}, booktitle = {New Trends in Knowledge Processing - Data Mining, Semantic Web and Computational Science. Proc. 6th Sanken International Symposium}, keywords = {2003 building from:hotho myown semantic web}, month = {MAR}, note = {March 10-11, 2003}, pages = {31-34}, title = {Building and Using the Semantic Web}, year = 2003 }

2002

Usage Mining for and on the Semantic Web. Stumme, Gerd; Berendt, Bettina; Hotho, Andreas (2002). 77-86.

[ BibTeX ] [ URL ]

@inproceedings{stumme02Usage, author = {Stumme, Gerd and Berendt, Bettina and Hotho, Andreas}, booktitle = {Next Generation Data Mining. Proc. NSF Workshop, Baltimore, Nov. 2002}, keywords = {2002 from:hotho mining myown semantic usage web wum}, pages = {77-86}, title = {Usage Mining for and on the Semantic Web}, year = 2002 }
Towards Semantic Web Mining. Berendt, B.; Hotho, A.; Stumme, G. in Lecture Notes in Computer Science (LNCS), I. Horrocks, Hendler, J. A. (Hrsg.) (2002). (Bd. 2342) 264-278.

[ BibTeX ]

@inproceedings{semweb-mining-iswc02, address = {Sardinia, Italy}, author = {Berendt, B. and Hotho, A. and Stumme, G.}, booktitle = {{P}roceedings of the {F}irst {I}nternational {S}emantic {W}eb {C}onference: The {S}emantic {W}eb ({ISWC} 2002)}, editor = {Horrocks, I. and Hendler, J. A.}, keywords = {2002 from:hotho mining myown semantic web}, pages = {264-278}, publisher = {Springer}, series = {Lecture Notes in Computer Science (LNCS)}, title = {Towards Semantic Web Mining}, volume = 2342, year = 2002 }
Semantic Web Mining for Building Information Portals (Position Paper). Hartmann, Jens; Hotho, Andreas; Stumme, Gerd (2002). 34-38.

[ BibTeX ]

@inproceedings{hartmann02Semantic, author = {Hartmann, Jens and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proc. Arbeitskreistreffen Knowledge Discovery, Oldenburg, Sept. 2002}, keywords = {2002 from:hotho mining myown portal semantic web}, pages = {34-38}, title = {Semantic Web Mining for Building Information Portals (Position Paper)}, year = 2002 }
Semantic Web Mining Berendt, B.; Hotho, A.; Stumme, G. (2002). Workshop at 13th Europ. Conf. on Machine Learning (ECML'02) / 6th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'02), Helsinki.

[ BibTeX ]

@proceedings{WS_BHS02, address = {Helsinki}, booktitle = {Proc. of the Semantic Web Mining Workshop}, editor = {Berendt, B. and Hotho, A. and Stumme, G.}, keywords = {2002 from:hotho mining myown semantic web}, month = {August 20}, publisher = {Workshop at 13th Europ. Conf. on Machine Learning (ECML'02) / 6th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'02)}, title = {Semantic Web Mining}, year = 2002 }
KAON - Towards a large scale Semantic Web. Ehrig, Marc; Handschuh, Siegfried; Hotho, Andreas; Maedche, Alexander; Motik, Boris; Oberle, Daniel; Schmitz, Christoph; Staab, Steffen; Stojanovic, Ljiljana; Stojanovic, Nenad; Studer, Rudi; Stumme, Gerd; Sure, York; Tane, Julien; Volz, Raphael; Zacharias, Valentin in LNCS, K. Bauknecht, Tjoa, A. M., Quirchmayr, G. (Hrsg.) (2002).

[ BibTeX ]

@inproceedings{ehrig2002kaon, address = {Aix-en-Provence %, France}, author = {Ehrig, Marc and Handschuh, Siegfried and Hotho, Andreas and Maedche, Alexander and Motik, Boris and Oberle, Daniel and Schmitz, Christoph and Staab, Steffen and Stojanovic, Ljiljana and Stojanovic, Nenad and Studer, Rudi and Stumme, Gerd and Sure, York and Tane, Julien and Volz, Raphael and Zacharias, Valentin}, booktitle = {Proc. E-Commerce and Web Technologies, Third International Conference, EC-Web 2002}, editor = {Bauknecht, Kurt and Tjoa, A. Min and Quirchmayr, Gerald}, keywords = {2002 from:hotho kaon knowledge management myown semantic web}, month = {September}, number = 2455, publisher = {Springer}, series = {LNCS}, title = {{KAON} - Towards a large scale {S}emantic {W}eb}, year = 2002 }
KAON - Towards a Large Scale Semantic Web. Bozsak, E.; Ehrig, M.; Handschuh, S.; Hotho, A.; Maedche, A.; Motik, B.; Oberle, D.; Schmitz, C.; Staab, S.; Stojanovic, L.; Stojanovic, N.; Studer, R.; Stumme, G.; Sure, Y.; Tane, J.; Volz, R.; Zacharias, V. in LNCS, K. Bauknecht, Tjoa, A. M., Quirchmayr, G. (Hrsg.) (2002). (Bd. 2455) 304--313.

[ BibTeX ]

@inproceedings{kaon_def2002, address = {Berlin}, author = {Bozsak, E. and Ehrig, M. and Handschuh, S. and Hotho, A. and Maedche, A. and Motik, B. and Oberle, D. and Schmitz, C. and Staab, S. and Stojanovic, L. and Stojanovic, N. and Studer, R. and Stumme, G. and Sure, Y. and Tane, J. and Volz, R. and Zacharias, V.}, booktitle = {E-Commerce and Web Technologies, Third International Conference, EC-Web 2002, Proceedings}, editor = {Bauknecht, K. and Tjoa, A. Min and Quirchmayr, G.}, keywords = {2002 SumSchool06 from:hotho kaon management myown semantic web}, pages = {304--313}, publisher = {Springer}, series = {LNCS}, title = {KAON - Towards a Large Scale Semantic Web}, volume = 2455, year = 2002 }

2001

Semantic Web Mining Stumme, G.; Hotho, A.; Berendt, B. (2001). Workshop at 12th Europ. Conf. on Machine Learning (ECML'01) / 5th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'01), Freiburg.

[ BibTeX ]

@proceedings{stumme_semwebmine_ws01, address = {Freiburg}, booktitle = {Proc. of the Semantic Web Mining Workshop}, editor = {Stumme, G. and Hotho, A. and Berendt, B.}, keywords = {2001 from:hotho mining myown semantic web}, month = {September 3rd}, publisher = {Workshop at 12th Europ. Conf. on Machine Learning (ECML'01) / 5th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'01)}, title = {Semantic Web Mining}, year = 2001 }
Ontology-based Text Clustering. Hotho, Andreas; Maedche, Alexander; Staab, Steffen (2001).

[ BibTeX ]

@inproceedings{hotho-ijcaiws2001, author = {Hotho, Andreas and Maedche, Alexander and Staab, Steffen}, booktitle = {Proc. of the Workshop ``Text Learning: Beyond Supervision'' at IJCAI 2001. Seattle, WA, USA, August 6, 2001}, keywords = {2001 SumSchool06 background clustering from:hotho knowledge myown ontology semantic text}, title = {Ontology-based Text Clustering}, year = 2001 }

2000

Semantic Community Web Portals. Staab, S.; Angele, J.; Decker, S.; Erdmann, M.; Hotho, A.; Maedche, A.; Schnurr, H.-P.; Studer, R.; Sure, Y. (2000). 473-491.

[ BibTeX ]

@inproceedings{staab.www9, author = {Staab, S. and Angele, J. and Decker, S. and Erdmann, M. and Hotho, A. and Maedche, A. and Schnurr, H.-P. and Studer, R. and Sure, Y.}, booktitle = {WWW9 --- Proceedings of the 9th International World Wide Web Conference, Amsterdam, The Netherlands}, keywords = {2000 community from:hotho myown portals semantic web}, pages = {473-491}, publisher = {Elsevier}, title = {Semantic Community Web Portals}, year = 2000 }
AI for the Web - Ontology-based Community Web Portals. Staab, Steffen; Angele, Jürgen; Decker, Stefan; Hotho, Andreas; Maedche, Alexander; Schnurr, Hans-Peter; Studer, Rudi; Sure, York (2000).

[ BibTeX ] [ URL ]

@inproceedings{Staab00AI, author = {Staab, Steffen and Angele, J{{ü}}rgen and Decker, Stefan and Hotho, Andreas and Maedche, Alexander and Schnurr, Hans-Peter and Studer, Rudi and Sure, York}, booktitle = {AAAI 2000/IAAI 2000 - Proceedings of the 17th National Conference on Artificial Intelligence and 12th Innovative Applications of Artificial Intelligence Conference, Austin/TX, USA, July 30-August 3, 2000}, keywords = {2000 community from:hotho myown ontology portals semantic web}, publisher = {AAAI Press/MIT Press}, title = {AI for the Web - Ontology-based Community Web Portals}, year = 2000 }

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