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Conference material: "Scientific service & Internet: proceedings of the 24th All-Russian Scientific Conference (September 19-22, 2022, online)"
Authors: Gusev D.I., Apanovich Z.V.
Impact of entity names embeddings on the quality of entity alignment
Abstract:
The problem of merging multilingual knowledge graphs (KG) is becoming more and more relevant. The main step for its solution is the identification of equivalent entities and their descriptions. It is also known as the entity alignment (EA) problem. In recent years, EA methods based on embeddings of entities have been actively studied. Recent studies show that the quality of these approaches depends on how information about the structure of knowledge graphs and methods for constructing embeddings of entity names are used. This article presents experiments, the purpose of which is to improve the alignment of entities on the English-Russian dataset.
Keywords:
multilingual knowledge graphs, entity alignment, embeddings, language model
Publication language: russian,  pages: 12 (p. 155-166)
Russian source text:
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About authors:
  • Gusev Daniil Ivanovich,  orcid.org/0000-0001-9636-2783,  Novosibirsk state university
  • Apanovich Zinaida Vladimirovna,  orcid.org/0000-0002-5767-284X,  Novosibirsk state university; A.P. Ershov Institute of Informatics Systems SBRAS