Semi-Supervised Entity Alignment With Global Alignment and Local Information Aggregation
Xuefeng Zhang, Richong Zhang, Junfan Chen, Jae‐In Kim, Yongyi Mao
Abstract
Entity alignment is a vital task in knowledge fusion, which aims to align entities from different knowledge graphs and merge them into one single graph. Existing entity alignment models focus on local features and try to minimize the distance between pairs of pre-aligned entities. Despite their success, these models heavily rely on the number of existing pre-aligned entity pairs and the topology information from the rest large set of unaligned entities is still largely unexplored. To overcome the limitation of existing models, we propose a model, termed Global Alignment and Local Information Aggregation, or GALA. GALA constructs global features for the knowledge graphs to be aligned using entity embeddings. It aligns the entities in the graphs by forcing their global features to match with each other and progressively updating the entity embeddings by aggregating local information from the other network. Empirical studies on commonly-used KG alignment data sets confirm the effectiveness of the proposed model.