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Ontology Learning and Knowledge Discovery Using the Web

Book Description

Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances provides relevant theoretical foundations, and disseminates new research findings and expert views on the remaining challenges in ontology learning. This book is invaluable resource as a library or personal reference for graduate students, researchers, and industrial practitioners. Readers who are in the process of looking for future research directions, and carving out their own niche area will find this book particularly useful due to the detailed scope and wide coverage of the book, which informs any discussion of artificial intelligence, knowledge acquisition, knowledge representation and reasoning, text mining, information extraction, and ontology learning.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
  5. Forward
  6. Preface
  7. Acknowledgment
  8. Section 1: Techniques for Ontology Learning and Knowledge Discovery
    1. Chapter 1: Evidence Sources, Methods and Use Cases for Learning Lightweight Domain Ontologies
      1. ABSTRACT
      2. 1 Introduction
      3. 2 Data Sources
      4. 3 Method
      5. 4 Use Cases
      6. 5 Outlook and Conclusions
    2. Chapter 2: An Overview of Shallow and Deep Natural Language Processing for Ontology Learning
      1. Abstract
      2. 1. Introduction
      3. 2. Background
      4. 3. NLP approches
      5. 5. Ontologizing the knowledge
      6. 6. Open Issues and FuTURE rESEARCH dIRECTIONS
      7. 7. Conclusion
    3. Chapter 3: Topic Extraction for Ontology Learning
      1. ABSTRACT
      2. Introduction
      3. State of the Art
      4. COMBINING THE TWO PHASES INTO AN INTEGRATED SYSTEM FOR EXTRACTING TOPICS
      5. CONCLUSION AND PERSPECTIVES
    4. Chapter 4: A Cognitive-Based Approach to Identify Topics in Text Using the Web as a Knowledge Source
      1. Abstract
      2. INTRODUCTION
      3. ISSUES WITH EXISTING METHODS
      4. COGNITIVE-BASED TOPICS IDENTIFICATION
      5. EXPERIMENTAL EVALUATION
      6. RESULTS AND DISCUSSION
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
    5. Chapter 5: Named Entity Recognition for Ontology Population using Background Knowledge from Wikipedia
      1. Abstract
      2. INTRODUCTION
      3. Named entity recognition and ontology population
      4. NER and background knowledge
      5. Tackling the Issues: A Novel Method
      6. Discussion and outlook
      7. Conclusion
    6. Chapter 6: User-Centered Maintenance of Concept Hierarchies
      1. ABSTRACT
      2. 1 Introduction
      3. 2 Related Work
      4. 3 Extension using Web Search Engines
      5. 4 Collaboratively Extending and Populating Concept Hierarchies
      6. 5 Visual Evaluation
      7. Analyzing Document Annotations
      8. Analyzing Automatic Annotations
      9. Review of the concept hierarchy
      10. Determining the focus of documents
      11. 6 Conclusion and Outlook
    7. Chapter 7: Learning SKOS Relations for Terminological Ontologies from Text
      1. ABSTRACT
      2. Introduction
      3. Related Work
      4. Ontology Categorisation
      5. Introduction to Latent Dirichlet Allocation
      6. Latent Semantic Analysis
      7. Probabilistic Latent Semantic Analysis
      8. Latent Dirichlet Allocation
      9. Learning Relations in Terminological Ontologies
      10. Concept Representation
      11. Information Theory Principle for Concept Relationship
      12. Connection to other Theories
      13. Ontology Learning Algorithm
      14. Experiment
      15. Dataset Preparation and Concept Extraction
      16. Learning LDA Models
      17. Folding-in Documents of Concepts
      18. Learning Terminological Ontologies
      19. Evaluation
      20. Evaluation Methods
      21. Evaluation of Results for “Broader” Relations
      22. Evaluation of Results for “Related” Relations
      23. Effect of Number of Sub-Nodes
      24. Discussion and Future Work
    8. Chapter 8: Incorporating Correlations among Gene Ontology Terms into Predicting Protein Functions
      1. Abstract
      2. INTRODUCTION
      3. Background
      4. Challenges in network-based protein function prediction
      5. Network-based protein function prediction
      6. Function Prediction with Hierarchically Independent Gene Ontology Terms
      7. CONCLUSIONS AND FUTURE DIRECTIONS
    9. Chapter 9: GO-Based Term Semantic Similarity
      1. ABSTRACT
      2. GENE ONTOLOGY AND GENE ONTOLOGY ANNOTATION
      3. SEMANTIC SIMILARITY BETWEEN GENE ONTOLOGY TERMS
      4. SEMANTIC SIMILARITY BETWEEN GENE PRODUCTS
      5. EVALUATION OF METHODS FOR COMPUTING SEMANTIC SIMILARITY BETWEEN GO TERMS
      6. PREVIOUS METHODS FOR COMPUTING SEMANTIC SIMILARITY BETWEEN GO TERMS
      7. A SEMANTIC SIMILARITY METHOD THAT DOES NOT RELY ON EXTERNAL DATA SOURCES
      8. Consider the Similarity Between the Definitions of GO Terms
      9. DISCUSSION AND FUTURE DIRECTIONS
    10. Chapter 10: ONTOLOGY LEARNING and the HUMANITIES
      1. Abstract
      2. INTRODUCTION
      3. FUTURE RESEARCH DIRECTIONS
      4. CONCLUSION
  9. Section 2: Applications of Ontologies and Knowledge Bases
    1. Chapter 11: Ontology-Based Knowledge Capture and Sharing in Enterprise Organisations
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. ONTOLOGY-BASED KNOWLEDGE MANAGEMENT
      5. EVALUATION
      6. CHALLENGES IN SEMANTIC KNOWLEDGE MANAGEMENT
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
  10. Section 3: Emerging Trends in Ontology Learning and Knowledge Discovery
    1. Chapter 12: Automated Learning of Social Ontologies
      1. Abstract
      2. INTRODUCTION
      3. Background
      4. Learning Social Ontologies
      5. Social Dimension of the Learned Ontologies
      6. Future Research Directions
      7. Trusting the Learning of Social Ontologies
      8. Conclusion
    2. Chapter 13: Mining Parallel Knowledge from Comparable Patents
      1. Abstract
      2. 1. INTRODUCTION
      3. 2. Background
      4. 3. The Comparable Patents and Its Preprocessing
      5. 4. Parallel Sentence Extraction
      6. 5. Bilingual Term Extraction
      7. 6. SMT Experiments
      8. 7. Discussion
      9. 8. Conclusion and Future Work
    3. Chapter 14: Cross-language Ontology Learning
      1. ABSTRACT
      2. INTRODUCTION
      3. Exploiting Cross-language Data
      4. Experimental Setup and Resources
      5. Our Ontology Learning Experiments
      6. Discussion
      7. Conclusion
      8. Feature Listing
  11. Compilation of References
  12. About the Contributors
  13. Index