Generative adversarial network for unsupervised multi-lingual knowledge graph entity alignment

Abstract

Entity alignment is an essential process in knowledge graph (KG) fusion, which aims to link entities representing the same real-world object in different KGs, to achieve entity expansion and graph fusion. Recently, embedding-based entity pair similarity evaluation has become mainstream in entity alignment research. However, these methods heavily rely on labelled entity pairs, which are often unavailable. Some self-supervised methods exploit features of KGs regardless of noise when generating aligned entity pairs. To resolve this issue, we propose a generative adversarial entity alignment method, which is more robust to noise data. The proposed method then exploits both attribute and structure information in the KGs and applies a BERT-based contrastive loss function to embed entities in KGs. Experimental results on several benchmark datasets demonstrate the superiority of our framework compared with most existing state-of-the-art entity alignment methods.