Guiding cross-lingual entity alignment via adversarial knowledge embedding

Xixun Lin, Hong Yang, Jia Wu, Chuan Zhou*, Bin Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Cross-lingual Entity Alignment (CEA) aims at identifying entities with their counterparts in different language knowledge graphs. Knowledge embedding alignment plays an important role in CEA due to its advantages of easy implementation and run-time robustness. However, existing embedding alignment methods haven't considered the problem of embedding distribution alignment which refers to the alignment of spatial shapes of embedding spaces. To this end, we present a new Adversarial Knowledge Embedding framework (AKE for short) that jointly learns the representation, mapping and adversarial modules in an end-to-end manner. By reducing the discrepancy of embedding distributions, AKE can approximately preserve an isomorphism between source and target embeddings. In addition, we introduce two new orthogonality constraints into mapping to obtain the self-consistency and numerical stability of transformation. Experiments on real-world datasets demonstrate that our method significantly outperforms state-of-the-art baselines.

Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
Place of PublicationLos Alamitos, CA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages429-438
Number of pages10
ISBN (Electronic)9781728146034
DOIs
Publication statusPublished - 2019
Event19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China
Duration: 8 Nov 201911 Nov 2019

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2019-November
ISSN (Print)1550-4786

Conference

Conference19th IEEE International Conference on Data Mining, ICDM 2019
CountryChina
CityBeijing
Period8/11/1911/11/19

Keywords

  • Cross-lingual-entity-alignment
  • Embedding-distribution
  • Knowledge-graph
  • Orthogonality-constraints

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  • Cite this

    Lin, X., Yang, H., Wu, J., Zhou, C., & Wang, B. (2019). Guiding cross-lingual entity alignment via adversarial knowledge embedding. In J. Wang, K. Shim, & X. Wu (Eds.), Proceedings - 19th IEEE International Conference on Data Mining, ICDM 2019 (pp. 429-438). (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2019-November). Los Alamitos, CA: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICDM.2019.00053