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Bias in Knowledge Graphs - An Empirical Study with Movie Recommendation and Different Language Editions of DBpedia

Authors Michael Matthias Voit, Heiko Paulheim

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Michael Matthias Voit
  • University of Mannheim, Germany
Heiko Paulheim
  • University of Mannheim, Germany

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Michael Matthias Voit and Heiko Paulheim. Bias in Knowledge Graphs - An Empirical Study with Movie Recommendation and Different Language Editions of DBpedia. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 14:1-14:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.LDK.2021.14

Abstract

Public knowledge graphs such as DBpedia and Wikidata have been recognized as interesting sources of background knowledge to build content-based recommender systems. They can be used to add information about the items to be recommended and links between those. While quite a few approaches for exploiting knowledge graphs have been proposed, most of them aim at optimizing the recommendation strategy while using a fixed knowledge graph. In this paper, we take a different approach, i.e., we fix the recommendation strategy and observe changes when using different underlying knowledge graphs. Particularly, we use different language editions of DBpedia. We show that the usage of different knowledge graphs does not only lead to differently biased recommender systems, but also to recommender systems that differ in performance for particular fields of recommendations.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Knowledge representation and reasoning
  • Information systems → Recommender systems
Keywords
  • Knowledge Graph
  • DBpedia
  • Recommender Systems
  • Bias
  • Language Bias
  • RDF2vec

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