@inproceedings{benz2010semantics, author = {Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge}, booktitle = {Proceedings of the 2nd Web Science Conference (WebSci10)}, address = {Raleigh, NC, USA}, year = {2010}, keywords = {2010, myown, ol, ontology, semantics, websci, websci10} }

%0 = inproceedings %A = Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = Proceedings of the 2nd Web Science Conference (WebSci10) %C = Raleigh, NC, USA %D = 2010 %T = Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge

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, author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, title = {Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems}, year = {2008}, url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0805.2045}, keywords = {2008, analysis, learning, myown, ol, ontology, semantic, similarity, tag}, 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.} }

%0 = misc %A = Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd %B = } %C = %D = 2008 %I = %T = Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems} %U = http://www.citebase.org/abstract?id=oai:arXiv.org:0805.2045

@inproceedings{Benz07OL, author = {Benz, Dominik and Hotho, Andreas}, title = {Position Paper: Ontology Learning from Folksonomies.}, editor = {Hinneburg, Alexander}, booktitle = {LWA 2007: Lernen - Wissen - Adaption, Halle, September 2007, Workshop Proceedings (LWA)}, publisher = {Martin-Luther-University Halle-Wittenberg}, year = {2007}, pages = {109-112}, url = {http://dblp.uni-trier.de/db/conf/lwa/lwa2007.html#BenzH07}, isbn = {978-3-86010-907-6}, keywords = {2007, folksonomy, kdubiq, learning, myown, ol, ontology, summerschool, webzu} }

%0 = inproceedings %A = Benz, Dominik and Hotho, Andreas %B = LWA 2007: Lernen - Wissen - Adaption, Halle, September 2007, Workshop Proceedings (LWA) %D = 2007 %I = Martin-Luther-University Halle-Wittenberg %T = Position Paper: Ontology Learning from Folksonomies. %U = http://dblp.uni-trier.de/db/conf/lwa/lwa2007.html#BenzH07

@inproceedings{voelker1:07:eswc, author = {Völker, Johanna and Vrandecic, Denny and Sure, York and Hotho, Andreas}, title = {Learning Disjointness}, editor = {Franconi, Enrico and Kifer, Michael and May, Wolfgang}, booktitle = {Proceedings of the European Semantic Web Conference, ESWC2007}, series = {Lecture Notes in Computer Science}, publisher = {Springer-Verlag}, year = {2007}, volume = {4519}, url = {http://www.eswc2007.org/pdf/eswc07-voelker1.pdf}, keywords = {2007, eswc, learning, myown, ol, ontology} }

%0 = inproceedings %A = Völker, Johanna and Vrandecic, Denny and Sure, York and Hotho, Andreas %B = Proceedings of the European Semantic Web Conference, ESWC2007 %D = 2007 %I = Springer-Verlag %T = Learning Disjointness %U = http://www.eswc2007.org/pdf/eswc07-voelker1.pdf

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.
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@incollection{bloehdorn2006learning, author = {Bloehdorn, Stephan and Cimiano, Philipp and Hotho, Andreas}, title = {Learning Ontologies to Improve Text Clustering and Classification}, booktitle = {From Data and Information Analysis to Knowledge Engineering}, publisher = {Springer Berlin Heidelberg}, year = {2006}, pages = {334--341}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/2006-03-gfkl05-bloehdorn-etal-learning-ontologies.pdf}, doi = {http://dx.doi.org/10.1007/3-540-31314-1_40}, isbn = {978-3-540-31313-7}, keywords = {2006, classification, clustering, myown, ol, text}, 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.
-} }

%0 = incollection %A = Bloehdorn, Stephan and Cimiano, Philipp and Hotho, Andreas %B = From Data and Information Analysis to Knowledge Engineering %D = 2006 %I = Springer Berlin Heidelberg %T = Learning Ontologies to Improve Text Clustering and Classification %U = http://www.kde.cs.uni-kassel.de/hotho/pub/2006/2006-03-gfkl05-bloehdorn-etal-learning-ontologies.pdf

@inproceedings{cim04a, author = {Cimiano, Philipp and Hotho, Andreas and Staab, Steffen}, title = {Clustering Ontologies from Text}, booktitle = {Proceedings of the Conference on Languages Resources and Evaluation (LREC)}, publisher = {ELRA - European Language Ressources Association}, address = {Lisbon, Portugal}, year = {2004}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2004/lrec04.pdf}, keywords = {2004, clustering, myown, ol, ontology, text} }

%0 = inproceedings %A = Cimiano, Philipp and Hotho, Andreas and Staab, Steffen %B = Proceedings of the Conference on Languages Resources and Evaluation (LREC) %C = Lisbon, Portugal %D = 2004 %I = ELRA - European Language Ressources Association %T = Clustering Ontologies from Text %U = http://www.kde.cs.uni-kassel.de/hotho/pub/2004/lrec04.pdf

@inproceedings{cim04b, author = {Cimiano, Philipp and Hotho, Andreas and Stumme, Gerd and Tane, Julien}, title = {Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies}, booktitle = {Proceedings of the The Second International Conference on Formal Concept Analysis (ICFCA 04)}, series = {LNCS}, publisher = {Springer}, year = {2004}, volume = {2961}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2004/icfca04.pdf}, isbn = {3-540-21043-1}, keywords = {2004, fca, myown, ol, ontology} }

%0 = inproceedings %A = Cimiano, Philipp and Hotho, Andreas and Stumme, Gerd and Tane, Julien %B = Proceedings of the The Second International Conference on Formal Concept Analysis (ICFCA 04) %D = 2004 %I = Springer %T = Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies %U = http://www.kde.cs.uni-kassel.de/hotho/pub/2004/icfca04.pdf