License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.LDK.2021.14
URN: urn:nbn:de:0030-drops-145506
URL: https://drops.dagstuhl.de/opus/volltexte/2021/14550/
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Voit, Michael Matthias ; Paulheim, Heiko

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

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OASIcs-LDK-2021-14.pdf (0.7 MB)


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.

BibTeX - Entry

@InProceedings{voit_et_al:OASIcs.LDK.2021.14,
  author =	{Voit, Michael Matthias and Paulheim, Heiko},
  title =	{{Bias in Knowledge Graphs - An Empirical Study with Movie Recommendation and Different Language Editions of DBpedia}},
  booktitle =	{3rd Conference on Language, Data and Knowledge (LDK 2021)},
  pages =	{14:1--14:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-199-3},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{93},
  editor =	{Gromann, Dagmar and S\'{e}rasset, Gilles and Declerck, Thierry and McCrae, John P. and Gracia, Jorge and Bosque-Gil, Julia and Bobillo, Fernando and Heinisch, Barbara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/14550},
  URN =		{urn:nbn:de:0030-drops-145506},
  doi =		{10.4230/OASIcs.LDK.2021.14},
  annote =	{Keywords: Knowledge Graph, DBpedia, Recommender Systems, Bias, Language Bias, RDF2vec}
}

Keywords: Knowledge Graph, DBpedia, Recommender Systems, Bias, Language Bias, RDF2vec
Collection: 3rd Conference on Language, Data and Knowledge (LDK 2021)
Issue Date: 2021
Date of publication: 30.08.2021
Supplementary Material: Software (Source Code): https://github.com/voitijaner/Movie-RSs-Master-Thesis-Submission-Voit archived at: https://archive.softwareheritage.org/swh:1:dir:5a1679a3579764cbd88c758be59c337e0f88a277


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