| Citation: | JI X，WU T X，WANG H G，et al. Attribute aggregation entity alignment based on multi-channel graph neural network[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（9）：2791-2799 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0703
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With the advent of the era of big data in power, power grid enterprises have accumulated a large number of multi-modal data in years of technical supervision work. The structured storage and fusion of multi-modal data are the keys to the effective organization and management of power systems. In order to fuse and construct a large-scale multi-modal knowledge graph of power defects, a multi-modal entity alignment method based on a multi-channel graph neural network was proposed to effectively fuse heterogeneous data of multi-source power. A multi-modal knowledge graph entity alignment dataset (EKG) for power defects was constructed by collecting logs related to many defects in the power field. Multi-modal information such as text and images was integrated into the knowledge graph, which provided rich semantic information for entity alignment tasks. The multi-modal data increased the representation difficulty of the entities and relationships. By mining the characteristics of the multi-modal knowledge graph in the power field, an attribute aggregation alignment method was designed. The node representation was learned from the four dimensions of image, text, name, and structure by using the multi-modal attributes and structure information in the knowledge graph, solving the problem that the power defects of a multi-modal knowledge graph cannot be integrated effectively. Experimental results show that the proposed method achieves the best performance on EKG.
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