Ontology Learning for the Semantic Web
Author:   Alexander Maedche
Publisher:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 2002
Volume:   665
ISBN:  

9781461353072


Pages:   244
Publication Date:   31 October 2012
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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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 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.

Full Product Details

Author:   Alexander Maedche
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 2002
Volume:   665
Dimensions:   Width: 15.50cm , Height: 1.40cm , Length: 23.50cm
Weight:   0.421kg
ISBN:  

9781461353072


ISBN 10:   1461353076
Pages:   244
Publication Date:   31 October 2012
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

I Fundamentals.- 1. Introduction.- 2. Ontology — Definition & Overview.- 3. Layered Ontology Engineering.- II Ontology Learning for the Semantic Web.- 4. Ontology Learning Framework.- 5. Data Import & Processing.- 6. Ontology Learning Algorithms.- III Implementation & Evaluation.- 7. The TEXT-TO-ONTO Environment.- 8. Evaluation.- IV Related Work & Outlook.- 9. Related Work.- 10. Conclusion & Outlook.- References.

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