Identifying Equivalent Relation Paths in Knowledge Graphs

  • Sameh K. Mohamed
  • Emir Muñoz
  • Vít Nováček
  • Pierre-Yves Vandenbussche
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10318)

Abstract

Relation paths are sequences of relations with inverse that allow for complete exploration of knowledge graphs in a two-way unconstrained manner. They are powerful enough to encode complex relationships between entities and are crucial in several contexts, such as knowledge base verification, rule mining, and link prediction. However, fundamental forms of reasoning such as containment and equivalence of relation paths have hitherto been ignored. Intuitively, two relation paths are equivalent if they share the same extension, i.e., set of source and target entity pairs. In this paper, we study the problem of containment as a means to find equivalent relation paths and show that it is very expensive in practice to enumerate paths between entities. We characterize the complexity of containment and equivalence of relation paths and propose a domain-independent and unsupervised method to obtain approximate equivalences ranked by a tri-criteria ranking function. We evaluate our algorithm using test cases over real-world data and show that we are able to find semantically meaningful equivalences efficiently.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sameh K. Mohamed
    • 1
  • Emir Muñoz
    • 1
    • 2
  • Vít Nováček
    • 1
  • Pierre-Yves Vandenbussche
    • 2
  1. 1.Insight Centre for Data Analytics at NUIGalwayIreland
  2. 2.Fujitsu Ireland LimitedDublinIreland

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