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MateTee: A Semantic Similarity Metric Based on Translation Embeddings for Knowledge Graphs

  • Camilo Morales
  • Diego CollaranaEmail author
  • Maria-Esther Vidal
  • Sören Auer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10360)

Abstract

Large Knowledge Graphs (KGs), e.g., DBpedia or Wikidata, are created with the goal of providing structure to unstructured or semi-structured data. Having these special datasets constantly evolving, the challenge is to utilize them in a meaningful, accurate, and efficient way. Further, exploiting semantics encoded in KGs, e.g., class and property hierarchies, provides the basis for addressing this challenge and producing a more accurate analysis of KG data. Thus, we focus on the problem of determining relatedness among entities in KGs, which corresponds to a fundamental building block for any semantic data integration task. We devise MateTee, a semantic similarity measure that combines the gradient descent optimization method with semantics encoded in ontologies, to precisely compute values of similarity between entities in KGs. We empirically study the accuracy of MateTee with respect to state-of-the-art methods. The observed results show that MateTee is competitive in terms of accuracy with respect to existing methods, with the advantage that background domain knowledge is not required.

Keywords

Gene Ontology Similarity Measure Connectivity Pattern Link Prediction Stochastic Gradient Descent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work is supported in part by the European Union under the Horizon 2020 Framework Program for the project BigDataEurope (GA 644564) as well as by the German Ministry of Education and Research with grant no. 13N13627 for the project LiDaKrA. We thank Mikhail Galkin for creating the DBpedia collection used in our experiments, and Ignacio Traverso Ribón for his support on the experimental comparison with GADES.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Camilo Morales
    • 1
    • 2
  • Diego Collarana
    • 1
    • 2
    Email author
  • Maria-Esther Vidal
    • 2
    • 3
  • Sören Auer
    • 1
    • 2
  1. 1.Enterprise Information Systems (EIS)University of BonnBonnGermany
  2. 2.Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS)Sankt AugustinGermany
  3. 3.Universidad Simón BolívarCaracasVenezuela

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