Entity and Relation Matching Consensus for Entity Alignment
Jinzhu Yang, Ding Wang, Wei Zhou, Wanhui Qian, Xin Wang, Jizhong Han, Songlin Hu
Abstract
Entity alignment aims to match synonymous entities across different knowledge graphs, which is a fundamental task for knowledge integration. Recently, researchers have devoted to leveraging rich information within relations to enhance entity alignment. They explicitly incorporate relations in entity representation and alignment, demonstrating remarkable results. However, affected by the semantic assumptions from early works, these works represent a relation by combining all the entities it connects, ignoring the semantic independence between entity and relation. Moreover, since these works perform alignment by comparing embedding similarity, they fail to consider a graph level alignment and tend to find local false correspondences.