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  • Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks (Extended Abstract)

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    Nguyen4474668-Accepted.pdf (410.3Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Nguyen, Thanh Tam
    Thanh Trung, Huynh
    yin, hongzhi
    TONG, Van Vinh
    Sakong, Darnbi
    Zheng, Bolong
    Nguyen, Quoc Viet Hung
    Griffith University Author(s)
    Nguyen, Henry
    Nguyen, Thanh Tam
    Year published
    2021
    Metadata
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    Abstract
    Knowledge graph (KG) entity alignment is the task of identifying corresponding entities across different KGs. Existing alignment techniques often require large amounts of labelled data, are unable to encode multi-modal data simultaneously, and enforce only a few consistency constraints. In this paper, we propose an end-to-end, unsupervised entity alignment framework for cross-lingual KGs using multi-order graph convolutional networks. An evaluation of our method using real-world datasets reveals that it consistently outperforms the state-of-the-art in terms of accuracy, efficiency, and label saving.Knowledge graph (KG) entity alignment is the task of identifying corresponding entities across different KGs. Existing alignment techniques often require large amounts of labelled data, are unable to encode multi-modal data simultaneously, and enforce only a few consistency constraints. In this paper, we propose an end-to-end, unsupervised entity alignment framework for cross-lingual KGs using multi-order graph convolutional networks. An evaluation of our method using real-world datasets reveals that it consistently outperforms the state-of-the-art in terms of accuracy, efficiency, and label saving.
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    Conference Title
    37th IEEE International Conference on Data Engineering
    DOI
    https://doi.org/10.1109/ICDE51399.2021.00247
    Copyright Statement
    © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Information systems
    Publication URI
    http://hdl.handle.net/10072/405994
    Collection
    • Conference outputs

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