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Volume 9 Issue 11
Nov.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Contents
L. Y. Yang, C. Lv, X. Wang, J. Qiao, W. P. Ding, J. Zhang, and  F.-Y. Wang,  “Collective entity alignment for knowledge fusion of power grid dispatching knowledge graphs,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 1990–2004, Nov. 2022. doi: 10.1109/JAS.2022.105947
Citation: L. Y. Yang, C. Lv, X. Wang, J. Qiao, W. P. Ding, J. Zhang, and  F.-Y. Wang,  “Collective entity alignment for knowledge fusion of power grid dispatching knowledge graphs,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 1990–2004, Nov. 2022. doi: 10.1109/JAS.2022.105947

Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

doi: 10.1109/JAS.2022.105947
Funds:  This work was supported by the National Key R&D Program of China (2018AAA0101502) and the Science and Technology Project of SGCC (State Grid Corporation of China): Fundamental Theory of Human-in-the-Loop Hybrid-Augmented Intelligence for Power Grid Dispatch and Control
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  • Knowledge graphs (KGs) have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services. In recent years, researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids. With multiple power grid dispatching knowledge graphs (PDKGs) constructed by different agencies, the knowledge fusion of different PDKGs is useful for providing more accurate decision supports. To achieve this, entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step. Existing entity alignment methods cannot integrate useful structural, attribute, and relational information while calculating entities’ similarities and are prone to making many-to-one alignments, thus can hardly achieve the best performance. To address these issues, this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments. This model proposes a novel knowledge graph attention network (KGAT) to learn the embeddings of entities and relations explicitly and calculates entities’ similarities by adaptively incorporating the structural, attribute, and relational similarities. Then, we formulate the counterpart assignment task as an integer programming (IP) problem to obtain one-to-one alignments. We not only conduct experiments on a pair of PDKGs but also evaluate our model on three commonly used cross-lingual KGs. Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

     

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    Highlights

    • A collective entity alignment model for the knowledge fusion of multiple power grid dispatching knowledge graphs is proposed
    • A novel knowledge graph attention network is proposed to learn the structural relatedness of entities and relations explicitly
    • The structural, attribute, and relational similarities between entities are adaptively integrated to obtain the synthesized similarities
    • Entities are collectively aligned with consistency and exclusiveness constraints based on a novel integer programming-based counterpart assignment strategy

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