European Knowledge Acquisition Workshop

EKAW 2016: Knowledge Engineering and Knowledge Management pp 666-680

Considering Semantics on the Discovery of Relations in Knowledge Graphs

  • Ignacio Traverso-Ribón
  • Guillermo Palma
  • Alejandro Flores
  • Maria-Esther Vidal
Conference paper

DOI: 10.1007/978-3-319-49004-5_43

Volume 10024 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Traverso-Ribón I., Palma G., Flores A., Vidal ME. (2016) Considering Semantics on the Discovery of Relations in Knowledge Graphs. In: Blomqvist E., Ciancarini P., Poggi F., Vitali F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science, vol 10024. Springer, Cham

Abstract

Knowledge graphs encode semantic knowledge that can be exploited to enhance different data-driven tasks, e.g., query answering, data mining, ranking or recommendation. However, knowledge graphs may be incomplete, and relevant relations may be not included in the graph, affecting accuracy of these data-driven tasks. We tackle the problem of relation discovery in a knowledge graph, and devise \(\mathcal {KOI}\), a semantic based approach able to discover relations in portions of knowledge graphs that comprise similar entities. \(\mathcal {KOI}\) exploits both datatype and object properties to compute the similarity among entities, i.e., two entities are similar if their datatype and object properties have similar values. \(\mathcal {KOI}\) implements graph partitioning techniques that exploit similarity values to discover relations from knowledge graph partitions. We conduct an experimental study on a knowledge graph of TED talks with state-of-the-art similarity measures and graph partitioning techniques. Our observed results suggest that \(\mathcal {KOI}\) is able to discover missing edges between related TED talks that cannot be discovered by state-of-the-art approaches. These results reveal that combining semantics encoded both in the similarity measures and in the knowledge graph structure, has a positive impact on the relation discovery problem.

Keywords

Relation discovery Semantic similarity Graph partitioning 

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ignacio Traverso-Ribón
    • 1
  • Guillermo Palma
    • 2
  • Alejandro Flores
    • 4
  • Maria-Esther Vidal
    • 2
    • 3
  1. 1.FZI Research Center for Information TechnologyKarlsruheGermany
  2. 2.Universidad Simón BolívarCaracasVenezuela
  3. 3.University of Bonn and FraunhoferBonnGermany
  4. 4.University of MarylandCollege ParkUSA