Ontology Learning for the Semantic Web

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Overview

Ontology Learning for the Semantic Web explores techniques for applying knowledge discovery techniques to different web data sources (such as HTML documents, dictionaries, etc.), in order to support the task of engineering and maintaining ontologies. The approach of ontology learning proposed in Ontology Learning for the Semantic Web includes a number of complementary disciplines that feed in different types of unstructured and semi-structured data. This data is necessary in ...

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Overview

Ontology Learning for the Semantic Web explores techniques for applying knowledge discovery techniques to different web data sources (such as HTML documents, dictionaries, etc.), in order to support the task of engineering and maintaining ontologies. The approach of ontology learning proposed in Ontology Learning for the Semantic Web includes a number of complementary disciplines that feed in different types of unstructured and semi-structured data. This data is necessary in order to support a semi-automatic ontology engineering process.
Ontology Learning for the Semantic Web is designed for researchers and developers of semantic web applications. It also serves as an excellent supplemental reference to advanced level courses in ontologies and the semantic web.

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Based upon the idea of applying knowledge discovery to multiple data sources to support the task of developing and maintaining ontologies, this work describes an approach that combines ontology engineering with machine learning. After discussing the history of ontologies, as well as their engineering and embedding into applications for the Semantic Web, the author establishes a general framework for ontology learning for the Semantic Web and introduces a layered ontology engineering framework. The implementation and evaluation of the proposed framework is then discussed and existing works that have similarities to the task of ontology learning are reviewed (such as information retrieval, information extraction, and machine learning to databases). Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Table of Contents

List of Figures. List of Tables. Preface. Acknowledgements. Foreword; R. Studer. Part I: Fundamentals. 1. Introduction. 2. Ontology - Definition & Overview. 3. Layered Ontology Engineering. Part II: Ontology Learning for the Semantic Web. 4. Ontology Learning Framework. 5. Data Import & Processing. 6. Ontology Learning Algorithms. Part III: Implementation & Evaluation. 7. The Text-To-Onto Environment. 8. Evaluation. Part IV: Related Work & Outlook. 9. Related Work. 10. Conclusion & Outlook. References. Index.

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