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Instance-Based Ontological Knowledge Acquisition

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7882)


The Linked Open Data (LOD) cloud contains tremendous amounts of interlinked instances, from where we can retrieve abundant knowledge. However, because of the heterogeneous and big ontologies, it is time consuming to learn all the ontologies manually and it is difficult to observe which properties are important for describing instances of a specific class. In order to construct an ontology that can help users easily access to various data sets, we propose a semi-automatic ontology integration framework that can reduce the heterogeneity of ontologies and retrieve frequently used core properties for each class. The framework consists of three main components: graph-based ontology integration, machine-learning-based ontology schema extraction, and an ontology merger. By analyzing the instances of the linked data sets, this framework acquires ontological knowledge and constructs a high-quality integrated ontology, which is easily understandable and effective in knowledge acquisition from various data sets using simple SPARQL queries.


  • Semantic Web
  • linked data
  • ontology integration
  • knowledge acquisition
  • machine learning


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Zhao, L., Ichise, R. (2013). Instance-Based Ontological Knowledge Acquisition. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds) The Semantic Web: Semantics and Big Data. ESWC 2013. Lecture Notes in Computer Science, vol 7882. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38287-1

  • Online ISBN: 978-3-642-38288-8

  • eBook Packages: Computer ScienceComputer Science (R0)