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Learning of OWL Class Expressions on Very Large Knowledge Bases and its Applications

Learning of OWL Class Expressions on Very Large Knowledge Bases and its Applications

Sebastian Hellmann, Jens Lehmann, Sören Auer
Copyright: © 2011 |Pages: 27
ISBN13: 9781609605933|ISBN10: 1609605934|EISBN13: 9781609605940
DOI: 10.4018/978-1-60960-593-3.ch005
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MLA

Hellmann, Sebastian, et al. "Learning of OWL Class Expressions on Very Large Knowledge Bases and its Applications." Semantic Services, Interoperability and Web Applications: Emerging Concepts, edited by Amit Sheth, IGI Global, 2011, pp. 104-130. https://doi.org/10.4018/978-1-60960-593-3.ch005

APA

Hellmann, S., Lehmann, J., & Auer, S. (2011). Learning of OWL Class Expressions on Very Large Knowledge Bases and its Applications. In A. Sheth (Ed.), Semantic Services, Interoperability and Web Applications: Emerging Concepts (pp. 104-130). IGI Global. https://doi.org/10.4018/978-1-60960-593-3.ch005

Chicago

Hellmann, Sebastian, Jens Lehmann, and Sören Auer. "Learning of OWL Class Expressions on Very Large Knowledge Bases and its Applications." In Semantic Services, Interoperability and Web Applications: Emerging Concepts, edited by Amit Sheth, 104-130. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-593-3.ch005

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Abstract

The vision of the Semantic Web aims to make use of semantic representations on the largest possible scale - the Web. Large knowledge bases such as DBpedia, OpenCyc, and GovTrack are emerging and freely available as Linked Data and SPARQL endpoints. Exploring and analysing such knowledge bases is a significant hurdle for Semantic Web research and practice. As one possible direction for tackling this problem, the authors present an approach for obtaining complex class expressions from objects in knowledge bases by using Machine Learning techniques. The chapter describes in detail how to leverage existing techniques to achieve scalability on large knowledge bases available as SPARQL endpoints or Linked Data. The algorithms are made available in the open source DL-Learner project and this chapter presents several real-life scenarios in which they can be used by Semantic Web applications.

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