Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources.

• Superior document: Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series ; v.44
• Author: Wohlgenannt, Gerhard.
• Place / Publishing House: Frankfurt a.M. : : Peter Lang GmbH, Internationaler Verlag der Wissenschaften,, 2011.; Ã2011.
• Year of Publication: 2011
• Edition: 1st ed.
• Language: English
• Series: Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series
• Physical Description: 1 online resource (226 pages)

Table of Contents

Cover -- 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.

4.6.5 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.