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A Semantically Driven Hybrid Network for Unsupervised Entity Alignment

Published: 16 March 2023 Publication History

Abstract

The major challenge in the task of entity alignment (EA) lies in the heterogeneity of the knowledge graph. The traditional solution to EA is to first map entities to the same space via knowledge embedding and then calculate the similarity between entities from different knowledge graphs. However, these methods mainly rely on manually labeled seeds of EA, which limits their applicability. Some researchers have begun using pseudo-labels rather than seeds for unsupervised EA. However, directly using pseudo-labels causes new problems, such as noise in the pseudo-labels. In this article, we propose a model called the Semantically Driven Hybrid Network (SDHN) to reduce the impact of noise in the pseudo-labels on the performance of EA models. The SDHN consists of two modules: a Teacher–Student Network (TSN) and a Rotation and Penalty (RAP) module. The TSN module reduces the impact of noise in two ways: (1) The TSN’s teacher network guides its student network to construct pseudo-labels based on semantic information instead of directly creating pseudo-labels. (2) It adaptively fuses semantic information into student networks to improve the final representation of entity embedding. Finally, the TSN enhances the performance of models of entity alignment via the RAP module. The results of experiments on multiple benchmark datasets showed that the SDHN outperforms state-of-the-art models.

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  1. A Semantically Driven Hybrid Network for Unsupervised Entity Alignment

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    Published In

    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 14, Issue 2
    April 2023
    430 pages
    ISSN:2157-6904
    EISSN:2157-6912
    DOI:10.1145/3582879
    • Editor:
    • Huan Liu
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 16 March 2023
    Online AM: 12 October 2022
    Accepted: 04 October 2022
    Revised: 30 September 2022
    Received: 28 March 2022
    Published in TIST Volume 14, Issue 2

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    Author Tags

    1. Knowledge graph
    2. graph neural networks
    3. entity alignment

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    Funding Sources

    • National Key Research and Development Program of China
    • National Natural Science Foundation of China
    • Beijing Academy of Artificial Intelligence (BAAI)

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