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Mid-Ontology Learning from Linked Data

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7185)

Abstract

The Linking Open Data(LOD) cloud is a collection of linked Resource Description Framework (RDF) data with over 26 billion RDF triples. Consuming linked data is a challenging task because each data set in the LOD cloud has specific ontology schema, and familiarity with ontology schema is required in order to query various linked data sets. However, manually checking each data set is time-consuming, especially when many data sets from various domains are used. This difficulty can be overcome without user interaction by using an automatic method that integrates different ontology schema. In this paper, we propose a Mid-Ontology learning approach that can automatically construct a simple ontology, linking related ontology predicates (class or property) in different data sets. Our Mid-Ontology learning approach consists of three main phases: data collection, predicate grouping, and Mid-Ontology construction. Experimental results show that our Mid-Ontology learning approach successfully integrates diverse ontology schema, and effectively retrieves related information.

Keywords

  • Mid-Ontology
  • linked data
  • semantic web
  • ontology learning

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Zhao, L., Ichise, R. (2012). Mid-Ontology Learning from Linked Data. In: Pan, J.Z., et al. The Semantic Web. JIST 2011. Lecture Notes in Computer Science, vol 7185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29923-0_8

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  • DOI: https://doi.org/10.1007/978-3-642-29923-0_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29922-3

  • Online ISBN: 978-3-642-29923-0

  • eBook Packages: Computer ScienceComputer Science (R0)