LEADER 05406nam 22007093 450 001 9910297042203321 005 20230914162438.0 010 $a3-631-75384-5 024 7 $a10.3726/b13903 035 $a(CKB)4100000007276974 035 $a(OAPEN)1003170 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/27633 035 $a(MiAaPQ)EBC30686223 035 $a(Au-PeEL)EBL30686223 035 $a(EXLCZ)994100000007276974 100 $a20230911d2011 uy 0 101 0 $aeng 135 $auuuuu---auuuu 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLearning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources 205 $aFirst edition. 210 1$aFrankfurt am Main :$cPeter Lang GmbH, Internationaler Verlag der Wissenschaften,$d2011. 210 4$dİ2011. 215 $a1 online resource (222 pages) 225 1 $aForschungsergebnisse der Wirtschaftsuniversitaet Wien. 311 $a3-631-60651-6 327 $aCover -- 1 Introduction -- 2 The Semantic Web -- 2.1 Overview -- 2.1.1 Background and Vision -- 2.1.2 Features -- 2.1.3 Misconceptions and Criticism -- 2.2 Applications -- 3 Ontologies -- 3.1 Fundamentals -- 3.1.1 Purpose -- 3.1.2 Structure and Entities -- 3.1.3 Ontology Research Fields -- 3.2 Representation -- 3.2.1 Resource Description Framework -- 3.2.2 RDF Schema -- 3.2.3 Web Ontology Language -- 3.3 Querying and Reasoning -- 3.3.1 SPARQL and RDQL -- 3.3.2 Reasoning with Jena -- 3.3.3 Redland -- 3.4 Public Datasets and Ontologies -- 3.4.1 DBpedia -- 3.4.2 Freebase -- 3.4.3 OpenCyc -- 4 Methodology -- 4.1 Ontology Learning -- 4.2 Methods for Learning Semantic Associations -- 4.2.1 Natural Language Processing Techniques -- 4.2.2 Lexico-syntactic Patterns -- 4.2.3 Relevant Statistical and Information Retrieval Measures and Methods -- 4.2.4 Machine Learning Paradigms -- 4.3 Literature Review -- 4.3.1 Domain Text and Semantic Associations -- 4.3.2 The Web and Semantic Associations -- 4.3.3 Domain Text and Linguistic Patterns -- 4.3.4 The Web and Linguistic Patterns -- 4.3.5 Semantic Web Data and Reasoning -- 4.3.6 Selected Work from SemEval2007 -- 4.3.7 Learning of Qualia Structures -- 4.4 webLyzard Ontology Learning System -- 4.4.1 System Overview -- 4.4.2 Major Components of the Framework -- 4.4.3 Identification of the Most Relevant Concepts -- 4.4.4 Concept Positioning and Taxonomy Discovery -- 4.5 A Novel Method to Detect Relations -- 4.5.1 Relation Labeling Based on Vector Space Similarity -- 4.5.2 Ontological Restrictions and Integration of External Knowledge -- 4.5.3 The Knowledge Base -- 4.5.4 A Hybrid Method for Relation Labeling -- 4.5.5 Integration of User Feedback -- 4.6 Implementation of the Method -- 4.6.1 Training -- 4.6.2 Compute Vector Space Similarities -- 4.6.3 Ontological Restrictions and Concept Grounding -- 4.6.4 Scarlet. 327 $a4.6.5 Evaluation -- 5 Results and Evaluation -- 5.1 Domain Relations and Domain Corpus -- 5.2 Evaluation of the Vector Space Model -- 5.2.1 Evaluation Baselines -- 5.2.2 Configuration Parameters -- 5.2.3 Average Ranking Precision -- 5.2.4 First Guess Correct -- 5.2.5 Second Guess Correct -- 5.3 Concept Grounding -- 5.4 Scarlet -- 5.5 Evaluation of Integrated Data Sources -- 5.5.1 Average Ranking Precision -- 5.5.2 First Guess Correct -- 5.5.3 Second Guess Correct -- 5.5.4 Individual Predicates -- 5.5.5 Summary and Interpretation -- 6 Conclusions and Outlook -- Bibliography. 330 $aThe manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach. 410 0$aForschungsergebnisse der Wirtschaftsuniversita?t Wien 606 $aSocial ethics$xInformation technology 606 $aBusiness enterprises 606 $aComputer software 610 $aBased 610 $aCombining 610 $aCorpus 610 $aData 610 $afrom 610 $aLearning 610 $amachine learning 610 $anatural language learning 610 $aOntology 610 $aReasoning 610 $arelation labeling 610 $aRelations 610 $aSemantic 610 $aSources 610 $aTechniques 610 $aWohlgenannt 615 0$aSocial ethics$xInformation technology. 615 0$aBusiness enterprises. 615 0$aComputer software. 700 $aWohlgenannt$b Gerhard$0881980 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910297042203321 996 $aLearning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources$91970156 997 $aUNINA