http://www.bibsonomy.org/burst/user/hotho/olBibSonomy BuRST Feed for /user/hotho/ol2008-07-21T01:06:36+02:00Institut AIFB - Publikation: Text2Onto - A Framework for Ontology Learning and Data-driven Change Discoveryhttp://www.bibsonomy.org/bibtex/2072436e5adc4f5fdc39f4baeaa55b077/hothohotho2008-05-20T21:48:14+02:00text2onto ontology kaon nlp learning ol <span style="color:#555555;">Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> and Johanna <a href="http://www.bibsonomy.org/author/Völker">V&#246;lker</a> </span><em>Proceedings of the 10th International Conference on Applications of Natural Language to Information Systems (NLDB), </em><em>volume3513ofLecture Notes in Computer Science, </em><em>page227-238. </em><em>Alicante, Spain, </em><em>Springer, </em><em>JUN2005. </em>Tue May 20 21:48:14 CEST 2008Alicante, SpainProceedings of the 10th International Conference on Applications of Natural Language to Information Systems (NLDB)JUN227-238Lecture Notes in Computer ScienceText2Onto - A Framework for Ontology Learning and Data-driven Change Discovery35132005text2onto ontology kaon nlp learning ol [0805.2045] Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systemshttp://www.bibsonomy.org/bibtex/278fd64c3db55e6387ebdeb6c40054542/hothohotho2008-05-19T08:38:59+02:00myown tag analysis 2008 similarity ol semantic ontology learning <span style="color:#555555;">Ciro <a href="http://www.bibsonomy.org/author/Cattuto">Cattuto</a> and Dominik <a href="http://www.bibsonomy.org/author/Benz">Benz</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span>(<em>2008</em>)Mon May 19 08:38:59 CEST 2008Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems2008myown tag analysis 2008 similarity ol semantic ontology learning 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.Semantic feature production norms for a large set ...[Behav Res Methods. 2005] - PubMed Resulthttp://www.bibsonomy.org/bibtex/2936af12b025e37b0a6aac6bc103f58a3/hothohotho2008-05-08T12:17:01+02:00ontology relation ol semantic dataset toread grounding <span style="color:#555555;">K <a href="http://www.bibsonomy.org/author/McRae">McRae</a> and G S <a href="http://www.bibsonomy.org/author/Cree">Cree</a> and M S <a href="http://www.bibsonomy.org/author/Seidenberg">Seidenberg</a> and C <a href="http://www.bibsonomy.org/author/McNorgan">McNorgan</a> </span><em>Behav Res Methods</em><em>37(4):547-559</em><em>Nov2005. </em>Thu May 08 12:17:01 CEST 2008Behav Res MethodsNov4547-559Semantic feature production norms for a large set of living and nonliving things372005ontology relation ol semantic dataset toread grounding Semantic features have provided insight into numerous behavioral phenomena concerning concepts, categorization, and semantic memory in adults, children, and neuropsychological populations. Numerous theories and models in these areas are based on representations and computations involving semantic features. Consequently, empirically derived semantic feature production norms have played, and continue to play, a highly useful role in these domains. This article describes a set of feature norms collected from approximately 725 participants for 541 living (dog) and nonliving (chair) basic-level concepts, the largest such set of norms developed to date. This article describes the norms and numerous statistics associated with them. Our aim is to make these norms available to facilitate other research, while obviating the need to repeat the labor-intensive methods involved in collecting and analyzing such norms. The full set of norms may be downloaded from www.psychonomic.org/archive.http://www.bibsonomy.org/bibtex/22d7c9ea5484ee45ea8bf3520138d7477/hothohotho2008-04-17T10:10:02+02:00learning myown ol fca 2005 text SumSchool06 <span style="color:#555555;">P. <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> and A. <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and S. <a href="http://www.bibsonomy.org/author/Staab">Staab</a> </span><em>Journal of Artificial Intelligence Research (JAIR)</em>(<em>2005</em>)Thu Apr 17 10:10:02 CEST 2008Journal of Artificial Intelligence Research (JAIR)305-339Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis242005learning myown ol fca 2005 text SumSchool06 SpringerLink - Book Chapterhttp://www.bibsonomy.org/bibtex/2121bdc75ba2f61db089bac5f715a07ba/hothohotho2008-02-28T10:16:38+01:00ol classification myown 2006 clustering text <span style="color:#555555;">Stephan <a href="http://www.bibsonomy.org/author/Bloehdorn">Bloehdorn</a> and Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>From Data and Information Analysis to Knowledge Engineering</em>(<em>2006</em>)Thu Feb 28 10:16:38 CET 2008From Data and Information Analysis to Knowledge Engineering334--341Learning Ontologies to Improve Text Clustering and Classification2006ol classification myown 2006 clustering text 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 -http://www.bibsonomy.org/bibtex/2c5c43ae4a719e6e935a9ca1a4aca906b/hothohotho2008-01-31T12:15:29+01:00myown eswc learning ol 2007 ontology <span style="color:#555555;">Johanna <a href="http://www.bibsonomy.org/author/Völker">V&#246;lker</a> and Denny <a href="http://www.bibsonomy.org/author/Vrandecic">Vrandecic</a> and York <a href="http://www.bibsonomy.org/author/Sure">Sure</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>Proceedings of the European Semantic Web Conference, ESWC2007, </em><em>volume4519ofLecture Notes in Computer Science, </em><em>Springer-Verlag, </em><em>July2007. </em>Thu Jan 31 12:15:29 CET 2008Proceedings of the European Semantic Web Conference, ESWC2007JulyLecture Notes in Computer Science{Learning Disjointness}45192007myown eswc learning ol 2007 ontology http://www.bibsonomy.org/bibtex/2ef6665b5a80e7eacd31a18f36408f9e6/hothohotho2008-01-15T10:22:47+01:002004 ontology fca myown ol <span style="color:#555555;">Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> and Julien <a href="http://www.bibsonomy.org/author/Tane">Tane</a> </span><em>Proceedings of the The Second International Conference on Formal Concept Analysis (ICFCA 04), </em><em>volume2961ofLNCS, </em><em>Springer, </em>(<em>2004</em>)Tue Jan 15 10:22:47 CET 2008Proceedings of the The Second International Conference on Formal Concept Analysis (ICFCA 04)LNCSConceptual Knowledge Processing with Formal Concept Analysis and Ontologies296120042004 ontology fca myown ol http://www.bibsonomy.org/bibtex/23bc6e5a51dba862da1b7b3b6ac563370/hothohotho2008-01-15T10:15:04+01:00text clustering myown ol ontology 2004 <span style="color:#555555;">Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> </span><em>Proceedings of the Conference on Languages Resources and Evaluation (LREC), </em><em>Lisbon, Portugal, </em><em>ELRA - European Language Ressources Association, </em><em>MAY2004. </em>Tue Jan 15 10:15:04 CET 2008Lisbon, PortugalProceedings of the Conference on Languages Resources and Evaluation (LREC)MAYClustering Ontologies from Text2004text clustering myown ol ontology 2004 Ontology Learning and Population from Texthttp://www.bibsonomy.org/bibtex/2fdfff52cddb448c0094213aff5bcaf31/hothohotho2007-06-05T12:00:48+02:00text ol <span style="color:#555555;">Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> </span><em>Springer-Verlag New York, Inc., </em><em>Secaucus, NJ, USA, </em>(<em>2006</em>)Tue Jun 05 12:00:48 CEST 2007Secaucus, NJ, USAOntology Learning and Population from Text: Algorithms, Evaluation and Applications2006text ol http://www.bibsonomy.org/bibtex/20e71ddd52894af0e681b9d9411f7944f/hothohotho2007-06-05T09:54:45+02:00learning ol dm ml web semantic <span style="color:#555555;">Paul <a href="http://www.bibsonomy.org/author/Buitelaar">Buitelaar</a> and Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> and Bernardo <a href="http://www.bibsonomy.org/author/Magnini">Magnini</a> (eds.). </span><em>Frontiers in Artificial Intelligence </em><em>IOS Press, </em><em>JUL2005. </em>Tue Jun 05 09:54:45 CEST 2007JULFrontiers in Artificial IntelligenceOntology Learning from Text: Methods, Evaluation and Applications1232005learning ol dm ml web semantic http://www.bibsonomy.org/bibtex/22b056b4ff9d1a7a0e1a361c56dae57d3/hothohotho2006-01-08T11:37:33+01:00ontology learning ol <span style="color:#555555;">A. <a href="http://www.bibsonomy.org/author/Maedche">Maedche</a> and S. <a href="http://www.bibsonomy.org/author/Staab">Staab</a> </span><em>IEEE Intelligent Systems</em><em>16(2):72--79</em>(<em>2001</em>)Sun Jan 08 11:37:33 CET 2006IEEE Intelligent Systems272--79Ontology Learning for the Semantic Web162001ontology learning ol