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A benchmark and comprehensive survey on knowledge graph entity alignment via representation learning

Abstract

In the last few years, the interest in knowledge bases has grown exponentially in both the research community and the industry due to their essential role in AI applications. Entity alignment is an important task for enriching knowledge bases. This paper provides a comprehensive tutorial-type survey on representative entity alignment techniques that use the new approach of representation learning. We present a framework for capturing the key characteristics of these techniques, propose a benchmark addressing the limitation of existing benchmark datasets, and conduct extensive experiments using our benchmark. The framework gives a clear picture of how various techniques work. The experiments yield important results about the empirical performance of the techniques and how various factors affect the performance. One important observation not stressed by previous work is that techniques making good use of attribute triples and relation predicates as features stand out as winners. We are also the first to investigate the question of how to perform entity alignments on large-scale knowledge graphs such as the full Wikidata and Freebase (in Experiment 5).

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Notes

  1. Our benchmark and all the code for our experiments are available at https://github.com/ruizhang-ai/EA_for_KG.

  2. https://wiki.dbpedia.org/downloads-2016-10.

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Zhang, R., Trisedya, B.D., Li, M. et al. A benchmark and comprehensive survey on knowledge graph entity alignment via representation learning. The VLDB Journal (2022). https://doi.org/10.1007/s00778-022-00747-z

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  • DOI: https://doi.org/10.1007/s00778-022-00747-z

Keywords

  • Knowledge graph
  • Entity alignment
  • Knowledge graph alignment
  • Knowledge base
  • Representation learning
  • Deep learning
  • Embedding
  • Graph neural networks
  • Graph convolutional networks