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Semi-Automatic Ontology Development

Book Description

The exploitation of theoretical results in knowledge representation, language standardization by W3C and data publication initiatives such as Linked Open Data have given a level of concreteness to the field of ontology research. In light of these recent outcomes, ontology development has also found its way to the forefront, benefiting from years of R&D on development tools. Semi-Automatic Ontology Development: Processes and Resources includes state-of-the-art research results aimed at the automation of ontology development processes and the reuse of external resources becoming a reality, thus being of interest for a wide and diversified community of users. This book provides a thorough overview on the current efforts on this subject and suggests common directions for interested researchers and practitioners.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. EDITORIAL ADVISORY BOARD
  5. Preface
    1. SECTION 1: KNOWLEDGE ACQUISITION SYSTEMS
    2. SECTION 2: RESOURCE ADOPTION AND REUSE TO BUILD ONTOLOGIES AND SEMANTIC REPOSITORIES
    3. SECTION 3: RELEVANT RESOURCES SUPPORTING ONTOLOGY DEVELOPMENT
  6. Section 1: Knowledge Acquisition Systems
    1. Chapter 1: Ontology-Based Information Extraction under a Bootstrapping Approach
      1. ABSTRACT
      2. INTRODUCTION
      3. RELATED WORK
      4. THE BOEMIE APPROACH FOR IE
      5. EVALUATION
      6. CONCLUSION
    2. Chapter 2: A Modular Framework to Learn Seed Ontologies from Text
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. A MODULAR FRAMEWORK TO LEARN SEED ONTOLOGIES FROM TEXT
      5. THE EXTRACTION TOOL
      6. QUANTITATIVE EVALUATION OF THE APPROACHES
      7. CONCLUSION AND FUTURE WORK
    3. Chapter 3: SODA
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MOTIVATION
      5. CODA
      6. SODA
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
  7. Section 2: Resource Adoption and Reuse to Build Ontologies and Semantic Repositories
    1. Chapter 4: Mining XML Schemas to Extract Conceptual Knowledge
      1. ABSTRACT
      2. INTRODUCTION
      3. XML DOCUMENTS AND XML SCHEMAS
      4. DERIVATION OF LOGICAL ASSERTIONS FROM XML SCHEMAS
      5. RELATED WORK
      6. TRANSFORMATION PATTERNS
      7. DERIVATION OF NAMES FOR OWL ENTITIES
      8. SYSTEM IMPLEMENTATION
      9. COMPARISON AND AUTOMATIC TRANSFORMATION EVALUATION
      10. CONCLUSION AND FUTURE WORK
    2. Chapter 5: LMF Dictionary-Based Approach for Domain Ontology Generation
      1. ABSTRACT
      2. INTRODUCTION
      3. STATE-OF-THE-ART AND MOTIVATIONS
      4. GENERATING DOMAIN ONTOLOGIES FROM LMF STANDARDIZED DICTIONARIES
      5. IMPLEMENTATION DETAILS
      6. A CASE STUDY: THE ASTRONOMY DOMAIN
      7. EVALUATION AND DISCUSSION
      8. CONCLUSION
    3. Chapter 6: OntoWiktionary
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ONTOWIKTIONARY
      5. HARVESTING KNOWLEDGE FROM WIKTIONARY
      6. ONTOLOGIZING THE KNOWLEDGE IN WIKTIONARY
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
    4. Chapter 7: Creation and Integration of Reference Ontologies for Efficient LOD Management
      1. ABSTRACT
      2. INTRODUCTION
      3. FROM ONTOLOGIES TO FACTFORGE
      4. REFERENCE KNOWLEDGE STACK (RKS)
      5. USE CASES
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
  8. Section 3: Relevant Resources Supporting Ontology Development
    1. Chapter 8: Aggregation and Maintenance of Multilingual Linked Data
      1. ABSTRACT
      2. INTRODUCTION
      3. WORDNET
      4. WORDNET IN XML AND RDF/OWL
      5. RDF/OWL EUROWORDNET
      6. INTERLINKING RDF/OWL-EUROWORDNET WITH OTHER LEXICAL RESOURCES
      7. THE LEXIRES RDF/OWL TOOL
      8. MULTILINGUAL ONTOLOGY-BASED USER MODELING
      9. CONCLUSION
    2. Chapter 9: Mining Multiword Terms from Wikipedia
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MINING MULTIWORD TERMS FROM WIKIPEDIA
      5. FUTURE RESEARCH DIRECTIONS
      6. CONCLUSION
    3. Chapter 10: Exploiting Transitivity in Probabilistic Models for Ontology Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. METHODS FOR ONTOLOGY LEARNING
      4. TRANSITIVITY IN A PROBABILISTIC MODEL
      5. GENERIC ONTOLOGY LEARNERS ON APPLICATION DOMAINS
      6. PROBABILISTIC ONTOLOGY LEARNER IN SEMANTIC TURKEY
      7. CONCLUSION AND FUTURE RESEARCH DIRECTIONS
  9. Compilation of References
  10. About the Contributors