Wombat – A Generalization Approach for Automatic Link Discovery

  • Mohamed Ahmed Sherif
  • Axel-Cyrille Ngonga Ngomo
  • Jens Lehmann
Conference paper

DOI: 10.1007/978-3-319-58068-5_7

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10249)
Cite this paper as:
Sherif M.A., Ngonga Ngomo AC., Lehmann J. (2017) Wombat – A Generalization Approach for Automatic Link Discovery. In: Blomqvist E., Maynard D., Gangemi A., Hoekstra R., Hitzler P., Hartig O. (eds) The Semantic Web. ESWC 2017. Lecture Notes in Computer Science, vol 10249. Springer, Cham

Abstract

A significant portion of the evolution of Linked Data datasets lies in updating the links to other datasets. An important challenge when aiming to update these links automatically under the open-world assumption is the fact that usually only positive examples for the links exist. We address this challenge by presenting and evaluating Wombat, a novel approach for the discovery of links between knowledge bases that relies exclusively on positive examples. Wombat is based on generalisation via an upward refinement operator to traverse the space of link specification. We study the theoretical characteristics of Wombat and evaluate it on 8 different benchmark datasets. Our evaluation suggests that Wombat outperforms state-of-the-art supervised approaches while relying on less information. Moreover, our evaluation suggests that Wombat’s pruning algorithm allows it to scale well even on large datasets.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mohamed Ahmed Sherif
    • 1
  • Axel-Cyrille Ngonga Ngomo
    • 1
    • 2
  • Jens Lehmann
    • 3
    • 4
  1. 1.R&D Department II, Computing CenterUniversity of LeipzigLeipzigGermany
  2. 2.Data Science GroupUniversity of PaderbornPaderbornGermany
  3. 3.Computer Science InstituteUniversity of BonnBonnGermany
  4. 4.Fraunhofer IAIS, Schloss BirlinghovenSankt AugustinGermany

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