基于多通道图神经网络的属性聚合式实体对齐

Attribute aggregation entity alignment based on multi-channel graph neural network

随着电力大数据时代的到来,电网企业在多年的技术监督工作中积累了大量的多模态数据。多模态数据的结构化存储和融合是电力系统有效组织和管理的关键。为融合构建大规模的电力缺陷多模态知识图谱,提出基于多通道图神经网络的多模态实体对齐方法,以有效融合多源电力异构数据。收集电力领域缺陷日志记录,构建电力缺陷多模态知识图谱实体对齐数据集(EKG),将文本、图像等多模态信息整合到知识图谱中,为实体对齐任务提供丰富的语义信息。多模态数据增加了实体和关系表示的难度,通过挖掘电力领域多模态知识图谱的特征信息,设计属性聚合式对齐方法,利用知识图谱中的多模态属性和结构信息从图像、文本、名称和结构4个维度学习节点表示,解决电力缺陷多模态知识图谱无法有效集成的问题。实验结果表明:所提方法在EKG上取得了最好的效果。

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 multichannel graph neural network was proposed to effectively fuse heterogeneous data of multi-source power.A multimodal 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 multimodal 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 multimodal knowledge graph cannot be integrated effectively.Experimental results show that the proposed method achieves the best performance on EKG.