Mid-Ontology Learning from Linked Data

  • Lihua Zhao
  • Ryutaro Ichise
Part of the Lecture Notes in Computer Science book series (LNCS, 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lihua Zhao
    • 1
  • Ryutaro Ichise
    • 1
  1. 1.Principles of Informatics Research DivisionNational Institute of InformaticsTokyoJapan

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