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    REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs

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    Type
    Conference Paper
    Authors
    Pei, Shichao
    Yu, Lu
    Yu, Guoxian
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    KAUST Grant Number
    URF/1/3756-01-01
    KAUST AI Initiative
    NSFC No. 61828302
    Date
    2020-08-20
    Online Publication Date
    2020-08-20
    Print Publication Date
    2020-08-23
    Permanent link to this record
    http://hdl.handle.net/10754/664816
    
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    Abstract
    Cross-lingual entity alignment aims at associating semantically similar entities in knowledge graphs with different languages. It has been an essential research problem for knowledge integration and knowledge graph connection, and been studied with supervised or semi-supervised machine learning methods with the assumption of clean labeled data. However, labels from human annotations often include errors, which can largely affect the alignment results. We thus aim to formulate and explore the robust entity alignment problem, which is non-trivial, due to the deficiency of noisy labels. Our proposed method named REA (Robust Entity Alignment) consists of two components: noise detection and noise-aware entity alignment. The noise detection is designed by following the adversarial training principle. The noise-aware entity alignment is devised by leveraging graph neural network based knowledge graph encoder as the core. In order to mutually boost the performance of the two components, we propose a unified reinforced training strategy to combine them. To evaluate our REA method, we conduct extensive experiments on several real-world datasets. The experimental results demonstrate the effectiveness of our proposed method and also show that our model consistently outperforms the state-of-the-art methods with significant improvement on alignment accuracy in the noise-involved scenario.
    Citation
    Pei, S., Yu, L., Yu, G., & Zhang, X. (2020). REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. doi:10.1145/3394486.3403268
    Sponsors
    The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), under award number URF/1/3756-01-01 and KAUST AI Initiative, and NSFC No. 61828302.
    Publisher
    Association for Computing Machinery (ACM)
    ISBN
    9781450379984
    DOI
    10.1145/3394486.3403268
    Additional Links
    https://dl.acm.org/doi/10.1145/3394486.3403268
    ae974a485f413a2113503eed53cd6c53
    10.1145/3394486.3403268
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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