Similarity propagation based semi-supervised entity alignment

Abstract

Entity alignment aims to identify entities referring to the same real world object among multiple knowledge graphs. Current embedding based approaches suffer from the lack of labeled entity pairs as training data. Some works attempt to boost the training process with semi-supervised methods, which add confidently predicted entity pairs into training data iteratively. Though the effectiveness of this strategy has been confirmed, the current semi-supervised methods suffer from the problem of incorrect newly labeled entity pairs. This paper presents a novel semi-supervised entity alignment framework Similarity Propagation based Semi-supervised Entity Alignment (SPSEA), which improves the precision of labeled entity pairs by propagating alignment information from seed entity pairs to their direct neighbors. The key idea is to combine dependency between entities and entity embeddings to obtain entity similarities, which alleviates the problem of mislabeling when the entity embeddings are of low quality. And it finds aligned entities from direct neighbors of seed entity pairs with the help of relation alignment, which narrows the search space and not hurting the recall of true pairs. In addition, we propose novel deferred-acceptance algorithm and bilateral alignment strategy to further guarantee the quality of obtained entity pairs. Through extensive experiments, we show that the quality of labeled entity pairs obtained by SPSEA is high than current semi-supervised methods.