Normalized Semantic Web Distance

  • Tom De Nies
  • Christian Beecks
  • Fréderic Godin
  • Wesley De Neve
  • Grzegorz Stepien
  • Dörthe Arndt
  • Laurens De Vocht
  • Ruben Verborgh
  • Thomas Seidl
  • Erik Mannens
  • Rik Van de Walle
Conference paper

DOI: 10.1007/978-3-319-34129-3_5

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)
Cite this paper as:
De Nies T. et al. (2016) Normalized Semantic Web Distance. In: Sack H., Blomqvist E., d'Aquin M., Ghidini C., Ponzetto S., Lange C. (eds) The Semantic Web. Latest Advances and New Domains. ESWC 2016. Lecture Notes in Computer Science, vol 9678. Springer, Cham

Abstract

In this paper, we investigate the Normalized Semantic Web Distance (NSWD), a semantics-aware distance measure between two concepts in a knowledge graph. Our measure advances the Normalized Web Distance, a recently established distance between two textual terms, to be more semantically aware. In addition to the theoretic fundamentals of the NSWD, we investigate its properties and qualities with respect to computation and implementation. We investigate three variants of the NSWD that make use of all semantic properties of nodes in a knowledge graph. Our performance evaluation based on the Miller-Charles benchmark shows that the NSWD is able to correlate with human similarity assessments on both Freebase and DBpedia knowledge graphs with values up to 0.69. Moreover, we verified the semantic awareness of the NSWD on a set of 20 unambiguous concept-pairs. We conclude that the NSWD is a promising measure with (1) a reusable implementation across knowledge graphs, (2) sufficient correlation with human assessments, and (3) awareness of semantic differences between ambiguous concepts.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Tom De Nies
    • 1
  • Christian Beecks
    • 2
  • Fréderic Godin
    • 1
  • Wesley De Neve
    • 1
    • 3
  • Grzegorz Stepien
    • 2
  • Dörthe Arndt
    • 1
  • Laurens De Vocht
    • 1
  • Ruben Verborgh
    • 1
  • Thomas Seidl
    • 2
  • Erik Mannens
    • 1
  • Rik Van de Walle
    • 1
  1. 1.iMinds – Data Science LabGhent UniversityGhentBelgium
  2. 2.DME GroupRWTH Aachen UniversityAachenGermany
  3. 3.IVY LabKAISTDaejeonRepublic of Korea

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