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.