
Ontology Learning for the Semantic Web
Alexander Maedche(Author)
Springer (Publisher)
Published on 31. October 2012
Book
Paperback/Softback
XXIII, 244 pages
978-1-4613-5307-2 (ISBN)
Description
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.
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.
More details
Series
Edition
Softcover reprint of the original 1st ed. 2002
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
XXIII, 244 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 15 mm
Weight
417 gr
ISBN-13
978-1-4613-5307-2 (9781461353072)
DOI
10.1007/978-1-4615-0925-7
Schweitzer Classification
Other editions
Additional editions

Alexander Maedche
Ontology Learning for the Semantic Web
E-Book
12/2012
Springer
€96.29
Available for download

Alexander Maedche
Ontology Learning for the Semantic Web
Book
02/2002
Kluwer Academic Publishers
€106.99
Shipment within 15-20 days
Content
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.