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Learning Commonalities in RDF

  • Sara El Hassad
  • François GoasdouéEmail author
  • Hélène Jaudoin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10249)

Abstract

Finding the commonalities between descriptions of data or knowledge is a foundational reasoning problem of Machine Learning introduced in the 70’s, which amounts to computing a least general generalization (\(\mathtt {lgg}\)) of such descriptions. It has also started receiving consideration in Knowledge Representation from the 90’s, and recently in the Semantic Web field. We revisit this problem in the popular Resource Description Framework (RDF) of W3C, where descriptions are RDF graphs, i.e., a mix of data and knowledge. Notably, and in contrast to the literature, our solution to this problem holds for the entire RDF standard, i.e., we do not restrict RDF graphs in any way (neither their structure nor their semantics based on RDF entailment, i.e., inference) and, further, our algorithms can compute \(\mathtt {lgg}\)s of small-to-huge RDF graphs.

Keywords

RDF RDFS RDF entailment Least general generalization 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sara El Hassad
    • 1
  • François Goasdoué
    • 1
    Email author
  • Hélène Jaudoin
    • 1
  1. 1.IRISA, Univ. Rennes 1LannionFrance

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