TY - CONF AU - Benz, Dominik AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge T2 - Proceedings of the 2nd Web Science Conference (WebSci10) PB - C1 - Raleigh, NC, USA PY - 2010/ CY - VL - IS - SP - EP - UR - DO - KW - 2010 KW - myown KW - ol KW - ontology KW - semantics KW - websci KW - websci10 L1 - SN - N1 - N1 - AB - ER - TY - GEN AU - Cattuto, Ciro AU - Benz, Dominik AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems JO - PB - C1 - PY - 2008/ VL - IS - SP - EP - UR - http://www.citebase.org/abstract?id=oai:arXiv.org:0805.2045 DO - KW - 2008 KW - analysis KW - learning KW - myown KW - ol KW - ontology KW - semantic KW - similarity KW - tag L1 - N1 - [0805.2045] Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems N1 - AB - 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. ER - TY - CONF AU - Benz, Dominik AU - Hotho, Andreas A2 - Hinneburg, Alexander T1 - Position Paper: Ontology Learning from Folksonomies. T2 - LWA 2007: Lernen - Wissen - Adaption, Halle, September 2007, Workshop Proceedings (LWA) PB - Martin-Luther-University Halle-Wittenberg C1 - PY - 2007/ CY - VL - IS - SP - 109 EP - 112 UR - http://dblp.uni-trier.de/db/conf/lwa/lwa2007.html#BenzH07 DO - KW - 2007 KW - folksonomy KW - kdubiq KW - learning KW - myown KW - ol KW - ontology KW - summerschool KW - webzu L1 - SN - 978-3-86010-907-6 N1 - dblp N1 - AB - ER - TY - CONF AU - Völker, Johanna AU - Vrandecic, Denny AU - Sure, York AU - Hotho, Andreas A2 - Franconi, Enrico A2 - Kifer, Michael A2 - May, Wolfgang T1 - Learning Disjointness T2 - Proceedings of the European Semantic Web Conference, ESWC2007 PB - Springer-Verlag C1 - PY - 2007/07 CY - VL - 4519 IS - SP - EP - UR - http://www.eswc2007.org/pdf/eswc07-voelker1.pdf DO - KW - 2007 KW - eswc KW - learning KW - myown KW - ol KW - ontology L1 - SN - N1 - N1 - AB - ER - TY - CHAP AU - Bloehdorn, Stephan AU - Cimiano, Philipp AU - Hotho, Andreas A2 - T1 - Learning Ontologies to Improve Text Clustering and Classification T2 - From Data and Information Analysis to Knowledge Engineering PB - Springer Berlin Heidelberg C1 - PY - 2006/ VL - IS - SP - 334 EP - 341 UR - http://www.kde.cs.uni-kassel.de/hotho/pub/2006/2006-03-gfkl05-bloehdorn-etal-learning-ontologies.pdf DO - http://dx.doi.org/10.1007/3-540-31314-1_40 KW - 2006 KW - classification KW - clustering KW - myown KW - ol KW - text L1 - SN - 978-3-540-31313-7 N1 - SpringerLink - Book Chapter N1 - AB - 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 - ER - TY - CONF AU - Cimiano, Philipp AU - Hotho, Andreas AU - Staab, Steffen A2 - T1 - Clustering Ontologies from Text T2 - Proceedings of the Conference on Languages Resources and Evaluation (LREC) PB - ELRA - European Language Ressources Association C1 - Lisbon, Portugal PY - 2004/05 CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/hotho/pub/2004/lrec04.pdf DO - KW - 2004 KW - clustering KW - myown KW - ol KW - ontology KW - text L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Cimiano, Philipp AU - Hotho, Andreas AU - Stumme, Gerd AU - Tane, Julien A2 - T1 - Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies T2 - Proceedings of the The Second International Conference on Formal Concept Analysis (ICFCA 04) PB - Springer C1 - PY - 2004/ CY - VL - 2961 IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/hotho/pub/2004/icfca04.pdf DO - KW - 2004 KW - fca KW - myown KW - ol KW - ontology L1 - SN - 3-540-21043-1 N1 - N1 - AB - ER -