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    Learning Short-Term Differences and Long-Term Dependencies for Entity Alignment

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    Name:
    ISWC2020-EntityAlignment[1].pdf
    Size:
    630.5Kb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Conference Paper
    Authors
    Chen, Jia
    Li, Zhixu
    Zhao, Pengpeng
    Liu, An
    Zhao, Lei
    Chen, Zhigang
    Zhang, Xiangliang cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Embargo End Date
    2021-12-02
    Permanent link to this record
    http://hdl.handle.net/10754/666228
    
    Metadata
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    Abstract
    We study the problem of structure-based entity alignment between knowledge graphs (KGs). The recent mainstream solutions for it apply KG embedding techniques to map entities into a vector space, where the similarity between entities could be measured accordingly. However, these methods which are mostly based on TransE and its variants treat relation triples in KGs independently. As a result, they fail to capture some advanced interactions between entities that are implicit in the surrounding and multi-hop entities: One is the differences between the one-hop and two-hop neighborhood of an entity, which we call as short-term differences, while the other is the dependencies between entities that are far apart, which we call as long-term dependencies. Based on the above observations, this paper proposes a novel approach learning to capture both the short-term differences and the long-term dependencies in KGs for entity alignment using graph neural networks and self-attention mechanisms respectively. Our empirical study conducted on four couples of real-world datasets shows the superiority of our model, compared with the state-of-the-art methods.
    Citation
    Chen, J., Li, Z., Zhao, P., Liu, A., Zhao, L., Chen, Z., & Zhang, X. (2020). Learning Short-Term Differences and Long-Term Dependencies for Entity Alignment. The Semantic Web – ISWC 2020, 92–109. doi:10.1007/978-3-030-62419-4_6
    Sponsors
    This research is partially supported by National Key R&D Program of China (No. 2018AAA0101900), the Priority Academic Program Development of Jiangsu Higher Education Institutions, Natural Science Foundation of Jiangsu Province (No. BK20191420), National Natural Science Foundation of China (Grant No. 62072323, 61632016), Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003, 18KJA520010).
    Publisher
    Springer International Publishing
    Conference/Event name
    19th International Semantic Web Conference, ISWC 2020
    ISBN
    9783030624187
    DOI
    10.1007/978-3-030-62419-4_6
    Additional Links
    http://link.springer.com/10.1007/978-3-030-62419-4_6
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-62419-4_6
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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