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

Bibliographic Details
Main Author: Wohlgenannt, Gerhard.
Format: eBook
Language:English
Published: Frankfurt a.M. : Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2011.
Edition:1st ed.
Series:Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series
Subjects:
Electronic books.
Online Access:Click to View
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.

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