Ontology Learning for the Semantic Web

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.

Summary

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.

Product details

• Authors: Alexander Maedche
• Publisher: Springer, Berlin
• Content: Book
• Product form: Hardback
• Publication date: 26.06.2009
• Subject: Natural sciences, medicine, IT, technology > IT, data processing > IT
• EAN: 9780792376569
• ISBN: 978-0-7923-7656-9
• Pages: 244
• Illustrations: XXIII, 244 p.
• Height (packing): 23.5 cm
• Weight (packing): 567 g
• Series: The Springer International Series in Engineering and Computer Science > 665; The Springer International Series in Engineering and Computer Science
• Subjects: B, Artificial Intelligence, Microprocessors, computer science, engineering, Professions, algorithms and data structures, Theory of Computation, Data Structures and Information Theory, Computers, Information theory, Mathematical theory of computation, Data structures (Computer science), Processor Architectures, Information technology: general topics, Computer architecture and logic design, Computer Architecture, The Computing Profession