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)


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
  • Knowledge Graph
  • DBpedia
  • Recommender Systems
  • Bias
  • Language Bias
  • RDF2vec


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  1. Patti Bao, Brent Hecht, Samuel Carton, Mahmood Quaderi, Michael Horn, and Darren Gergle. Omnipedia: bridging the wikipedia language gap. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1075-1084, 2012. Google Scholar
  2. Pablo Beytía. The positioning matters: Estimating geographical bias in the multilingual record of biographies on wikipedia. In Companion Proceedings of the Web Conference 2020, pages 806-810, 2020. Google Scholar
  3. Ewa S Callahan and Susan C Herring. Cultural bias in wikipedia content on famous persons. Journal of the American society for information science and technology, 62(10):1899-1915, 2011. Google Scholar
  4. Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua. Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In The world wide web conference, pages 151-161, 2019. Google Scholar
  5. Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the fourth ACM conference on Recommender systems, pages 39-46, 2010. Google Scholar
  6. Amine Dadoun, Raphaël Troncy, Olivier Ratier, and Riccardo Petitti. Location embeddings for next trip recommendation. In Companion Proceedings of The 2019 World Wide Web Conference, pages 896-903, 2019. Google Scholar
  7. Tommaso Di Noia, Iván Cantador, and Vito Claudio Ostuni. Linked open data-enabled recommender systems: Eswc 2014 challenge on book recommendation. In Semantic Web Evaluation Challenge, pages 129-143. Springer, 2014. Google Scholar
  8. Tommaso Di Noia, Roberto Mirizzi, Vito Claudio Ostuni, and Davide Romito. Exploiting the web of data in model-based recommender systems. In Proceedings of the sixth ACM conference on Recommender systems, pages 253-256, 2012. Google Scholar
  9. Young-Ho Eom, Pablo Aragón, David Laniado, Andreas Kaltenbrunner, Sebastiano Vigna, and Dima L Shepelyansky. Interactions of cultures and top people of wikipedia from ranking of 24 language editions. PloS one, 10(3):e0114825, 2015. Google Scholar
  10. Stephen Follows. The relative popularity of genres around the world:, 2016. URL:
  11. Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. A survey on knowledge graph-based recommender systems. IEEE Transactions on Knowledge and Data Engineering, 2020. Google Scholar
  12. Noriko Hara, Pnina Shachaf, and Khe Foon Hew. Cross-cultural analysis of the wikipedia community. Journal of the American Society for Information Science and Technology, 61(10):2097-2108, 2010. Google Scholar
  13. F Maxwell Harper and Joseph A Konstan. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1-19, 2015. Google Scholar
  14. Ming He, Bo Wang, and Xiangkun Du. Hi2rec: Exploring knowledge in heterogeneous information for movie recommendation. IEEE Access, 7:30276-30284, 2019. Google Scholar
  15. Brent Hecht and Darren Gergle. Measuring self-focus bias in community-maintained knowledge repositories. In Proceedings of the fourth international conference on communities and technologies, pages 11-20, 2009. Google Scholar
  16. Nicolas Heist, Sven Hertling, Daniel Ringler, and Heiko Paulheim. Knowledge Graphs on the Web - an Overview, pages 3-22. IOS Press, 2020. Google Scholar
  17. Nicolas Heist and Heiko Paulheim. Uncovering the semantics of wikipedia categories. In International semantic web conference, pages 219-236. Springer, 2019. Google Scholar
  18. Sven Hertling and Heiko Paulheim. Dbkwik: A consolidated knowledge graph from thousands of wikis. In 2018 IEEE International Conference on Big Knowledge (ICBK), pages 17-24. IEEE, 2018. Google Scholar
  19. Hen-Hsen Huang. An mpd player with expert knowledge-basedsingle user music recommendation. In IEEE/WIC/ACM International Conference on Web Intelligence-Companion Volume, pages 318-321, 2019. Google Scholar
  20. Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, and Edward Y Chang. Improving sequential recommendation with knowledge-enhanced memory networks. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pages 505-514, 2018. Google Scholar
  21. Freddy Lecue. On the role of knowledge graphs in explainable ai. Semantic Web, 11(1):41-51, 2020. Google Scholar
  22. Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al. Dbpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Semantic web, 6(2):167-195, 2015. Google Scholar
  23. Florian Lemmerich, Diego Sáez-Trumper, Robert West, and Leila Zia. Why the world reads wikipedia: Beyond english speakers. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pages 618-626, 2019. Google Scholar
  24. Włodzimierz Lewoniewski, Krzysztof Węcel, and Witold Abramowicz. Quality and importance of wikipedia articles in different languages. In International Conference on Information and Software Technologies, pages 613-624. Springer, 2016. Google Scholar
  25. Qika Lin, Yaoqiang Niu, Yifan Zhu, Hao Lu, Keith Zvikomborero Mushonga, and Zhendong Niu. Heterogeneous knowledge-based attentive neural networks for short-term music recommendations. IEEE Access, 6:58990-59000, 2018. Google Scholar
  26. Paolo Massa and Federico Scrinzi. Manypedia: Comparing language points of view of wikipedia communities. In Proceedings of the Eighth Annual International Symposium on Wikis and Open Collaboration, pages 1-9, 2012. Google Scholar
  27. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111-3119, 2013. Google Scholar
  28. Volodymyr Miz, Joëlle Hanna, Nicolas Aspert, Benjamin Ricaud, and Pierre Vandergheynst. What is trending on wikipedia? capturing trends and language biases across wikipedia editions. In Companion Proceedings of the Web Conference 2020, pages 794-801, 2020. Google Scholar
  29. Cataldo Musto, Pierpaolo Basile, and Giovanni Semeraro. Embedding knowledge graphs for semantics-aware recommendations based on dbpedia. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, pages 27-31, 2019. Google Scholar
  30. Tommaso Di Noia, Vito Claudio Ostuni, Paolo Tomeo, and Eugenio Di Sciascio. Sprank: Semantic path-based ranking for top-n recommendations using linked open data. ACM Transactions on Intelligent Systems and Technology (TIST), 8(1):1-34, 2016. Google Scholar
  31. Enrico Palumbo, Giuseppe Rizzo, and Raphaël Troncy. Entity2rec: Learning user-item relatedness from knowledge graphs for top-n item recommendation. In Proceedings of the eleventh ACM conference on recommender systems, pages 32-36, 2017. Google Scholar
  32. Alexandre Passant. dbrec - music recommendations using dbpedia. In International Semantic Web Conference, pages 209-224. Springer, 2010. Google Scholar
  33. Heiko Paulheim. Generating possible interpretations for statistics from linked open data. In Extended Semantic Web Conference, pages 560-574. Springer, 2012. Google Scholar
  34. Guangyuan Piao and John G Breslin. Exploring dynamics and semantics of user interests for user modeling on twitter for link recommendations. In proceedings of the 12th international conference on semantic systems, pages 81-88, 2016. Google Scholar
  35. Francesco Ricci, Lior Rokach, and Bracha Shapira. Recommender systems: introduction and challenges. In Recommender systems handbook, pages 1-34. Springer, 2015. Google Scholar
  36. Petar Ristoski, Eneldo Loza Mencía, and Heiko Paulheim. A hybrid multi-strategy recommender system using linked open data. In Semantic Web Evaluation Challenge, pages 150-156. Springer, 2014. Google Scholar
  37. Petar Ristoski and Heiko Paulheim. Semantic web in data mining and knowledge discovery: A comprehensive survey. Journal of Web Semantics, 36:1-22, 2016. Google Scholar
  38. Petar Ristoski, Jessica Rosati, Tommaso Di Noia, Renato De Leone, and Heiko Paulheim. Rdf2vec: Rdf graph embeddings and their applications. Semantic Web, 10(4):721-752, 2019. Google Scholar
  39. Jessica Rosati, Petar Ristoski, Tommaso Di Noia, Renato de Leone, and Heiko Paulheim. Rdf graph embeddings for content-based recommender systems. In CEUR workshop proceedings, volume 1673, pages 23-30. RWTH, 2016. Google Scholar
  40. Xiaoli Tang, Tengyun Wang, Haizhi Yang, and Hengjie Song. Akupm: Attention-enhanced knowledge-aware user preference model for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1891-1899, 2019. Google Scholar
  41. Denny Vrandečić and Markus Krötzsch. Wikidata: a free collaborative knowledgebase. Communications of the ACM, 57(10):78-85, 2014. Google Scholar
  42. Claudia Wagner, David Garcia, Mohsen Jadidi, and Markus Strohmaier. It’s a man’s wikipedia? assessing gender inequality in an online encyclopedia. arXiv preprint, 2015. URL:
  43. Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 417-426, 2018. Google Scholar
  44. Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. Dkn: Deep knowledge-aware network for news recommendation. In Proceedings of the 2018 world wide web conference, pages 1835-1844, 2018. Google Scholar
  45. Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, and Zhongyuan Wang. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 968-977, 2019. Google Scholar
  46. Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. Multi-task feature learning for knowledge graph enhanced recommendation. In The World Wide Web Conference, pages 2000-2010, 2019. Google Scholar
  47. Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. Knowledge graph convolutional networks for recommender systems. In The world wide web conference, pages 3307-3313, 2019. Google Scholar
  48. Meng Wang, Mengyue Liu, Jun Liu, Sen Wang, Guodong Long, and Buyue Qian. Safe medicine recommendation via medical knowledge graph embedding. arXiv preprint, 2017. URL:
  49. Xinyu Wang, Ying Zhang, Xiaoling Wang, and Jin Chen. A knowledge graph enhanced topic modeling approach for herb recommendation. In International Conference on Database Systems for Advanced Applications, pages 709-724. Springer, 2019. Google Scholar
  50. Krzysztof Węcel and Włodzimierz Lewoniewski. Modelling the quality of attributes in wikipedia infoboxes. In International Conference on Business Information Systems, pages 308-320. Springer, 2015. Google Scholar
  51. Hartmut Wessler, Christoph Kilian Theil, Heiner Stuckenschmidt, Angelika Storrer, and Marc Debus. Wikiganda: Detecting bias in multimodal wikipedia entries. New Studies in Multimodality. London/New York: Bloomsbury, pages 201-224, 2017. Google Scholar
  52. Deqing Yang, Zikai Guo, Ziyi Wang, Juyang Jiang, Yanghua Xiao, and Wei Wang. A knowledge-enhanced deep recommendation framework incorporating gan-based models. In 2018 IEEE International Conference on Data Mining (ICDM), pages 1368-1373. IEEE, 2018. Google Scholar
  53. Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 353-362, 2016. Google Scholar
  54. Yongfeng Zhang, Qingyao Ai, Xu Chen, and Pengfei Wang. Learning over knowledge-base embeddings for recommendation. Algorithms, 11(9), 2018. Google Scholar
  55. Yiwei Zhou, Elena Demidova, and Alexandra I Cristea. Who likes me more? analysing entity-centric language-specific bias in multilingual wikipedia. In Proceedings of the 31st Annual ACM Symposium on Applied Computing, pages 750-757, 2016. Google Scholar
  56. Zili Zhou, Shaowu Liu, Guandong Xu, Xing Xie, Jun Yin, Yidong Li, and Wu Zhang. Knowledge-based recommendation with hierarchical collaborative embedding. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 222-234. Springer, 2018. Google Scholar
  57. Guiming Zhu, Chenzhong Bin, Tianlong Gu, Liang Chang, Yanpeng Sun, Wei Chen, and Zhonghao Jia. A neural user preference modeling framework for recommendation based on knowledge graph. In Pacific Rim International Conference on Artificial Intelligence, pages 176-189. Springer, 2019. Google Scholar
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