Entity Alignment across Power Knowledge Graphs
Yibo Chen, Fan Xiong, Feng He Wu, Xing Xiang, Jun Yao Gao, Jianliang Gao · 2020
Power knowledge graph (PKG) plays an essential role in the electricity power field to integrate multi-source heterogeneous power data in a structural form. Recently, PKG is widely used for management and analysis of electric power big data. However, a single PKG can only provide limited power knowledge, which restricted its functions in downstream applications. In this paper, we merge multiple power knowledge graphs by aligning the entities which belong to different sources or languages respectively. To align entities, a Typed Graph Attention Network method is proposed to learn neighbor-level embedding through the weight of relation types in power knowledge graph. By incorporating structural information and relation type information simultaneously, the proposed method can accurately capture entity features in different Power knowledge graph.