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Ontologies for Machine Learning

In: Handbook on Ontologies

Author

Listed:
  • Stephan Bloehdorn

    (Institute AIFB, University of Karlsruhe)

  • Andreas Hotho

    (Department of Mathematics and Computer Science, University of Kassel)

Abstract

Summary The growing amounts of ontologies and semantically annotated data has led to considerable interest in mining these richly structured data sources. While research has actively addressed the issue of inducing semantic structures from conventional types of data, approaches for mining semantically annotated data still constitute an emerging field of research. Approaches in this direction either investigate how semantic structures can help to advance classical Machine Learning tasks or how semantic structures can themselves become the objects of interest. In this chapter, we review some of the main topics at the intersection of Machine Learning and Semantic Web research.

Suggested Citation

  • Stephan Bloehdorn & Andreas Hotho, 2009. "Ontologies for Machine Learning," International Handbooks on Information Systems, in: Steffen Staab & Rudi Studer (ed.), Handbook on Ontologies, pages 637-661, Springer.
    • Handle: RePEc:spr:ihichp:978-3-540-92673-3_29
    DOI: 10.1007/978-3-540-92673-3_29
    as

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