Self-Learning and Embedding Based Entity Alignment

Abstract

Entity alignment aims to identify semantical matchings between entities from different groups. Traditional methods (e.g., attribute comparison based methods, clustering based methods, and active learning methods) are usually supervised by labelled data as prior knowledge. Since it is not trivial to label data for training, researchers have recently turned to unsupervised methods, and have thus developed similarity based methods, probabilistic methods, hierarchical graph model based methods, etc. As an important part of a knowledge graph, entities contain rich semantical information that can be well learned by knowledge graph embedding in a low-dimensional vector space. However, existing methods for entity alignment have paid little attention to knowledge graph embedding. In this paper, we propose a Self-learning and Embedding based method for Entity Alignment, thus called SEEA, to iteratively find semantically matched entity pairs, which makes full use of semantical information contained in the attributes of entities. Experiments on two realistic datasets and comparison with the baselines validate the effectiveness and merits of the proposed method.