Iterative Entity Alignment via Joint Knowledge Embeddings

Iterative Entity Alignment via Joint Knowledge Embeddings

Hao Zhu, Ruobing Xie, Zhiyuan Liu, Maosong Sun

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 4258-4264. https://doi.org/10.24963/ijcai.2017/595

Entity alignment aims to link entities and their counterparts among multiple knowledge graphs (KGs). Most existing methods typically rely on external information of entities such as Wikipedia links and require costly manual feature construction to complete alignment. In this paper, we present a novel approach for entity alignment via joint knowledge embeddings. Our method jointly encodes both entities and relations of various KGs into a unified low-dimensional semantic space according to a small seed set of aligned entities. During this process, we can align entities according to their semantic distance in this joint semantic space. More specifically, we present an iterative and parameter sharing method to improve alignment performance. Experiment results on real-world datasets show that, as compared to baselines, our method achieves significant improvements on entity alignment, and can further improve knowledge graph completion performance on various KGs with the favor of joint knowledge embeddings.
Keywords:
Natural Language Processing: Information Extraction
Natural Language Processing: Natural Language Semantics
Natural Language Processing: Natural Language Processing