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

arXiv: https://arxiv.org/abs/2105.00674 · HTML: https://mlsanity.com/paper/2105.00674/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 58 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 16 of 16)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [DKN: Deep Knowledge-Aware Network for News Recommendation](https://mlsanity.com/paper/1801.08284/) (2018)
- [Knowledge Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1904.12575/) (2019)
- [Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems](https://mlsanity.com/paper/1905.04413/) (2019)
- [Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences](https://mlsanity.com/paper/1902.06236/) (2019)
- [Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation](https://mlsanity.com/paper/1901.08907/) (2019)
- [Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation](https://mlsanity.com/paper/1805.03352/) (2018)
- [It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopedia](https://mlsanity.com/paper/1501.06307/) (2015)
- [Knowledge Graphs on the Web -- an Overview](https://mlsanity.com/paper/2003.00719/) (2020)
- [SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation](https://mlsanity.com/paper/1710.05980/) (2017)
- [Why the World Reads Wikipedia: Beyond English Speakers](https://mlsanity.com/paper/1812.00474/) (2018)
- [Uncovering the Semantics of Wikipedia Categories](https://mlsanity.com/paper/1906.12089/) (2019)
- [What is Trending on Wikipedia? Capturing Trends and Language Biases Across Wikipedia Editions](https://mlsanity.com/paper/2002.06885/) (2020)
- [Interactions of cultures and top people of Wikipedia from ranking of 24 language editions](https://mlsanity.com/paper/1405.7183/) (2014)

## Abstract (arXiv)

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.