Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources.
The 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 relation...
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Superior document: | Forschungsergebnisse der Wirtschaftsuniversitaet Wien. |
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Place / Publishing House: | Frankfurt am Main : : Peter Lang GmbH, Internationaler Verlag der Wissenschaften,, 2011. ©2011. |
Year of Publication: | 2011 |
Edition: | First edition. |
Language: | English |
Series: | Forschungsergebnisse der Wirtschaftsuniversität Wien
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Physical Description: | 1 online resource (222 pages) |
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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.