Entity alignment based on relational semantics augmentation for multilingual knowledge graphs

Seeking similar entity pairs pointing to their counterpart real-world objects in knowledge graphs (KGs) is one of the most challenging and critical steps for entity alignment (EA), also known as KG integration. EA has prompted the development of knowledge-based technologies such as recommender systems, semantic search engines, chatbot systems, and knowledge reasoning. Recent years have witnessed increasing interest in representation learning-based entity alignment methods. Most of these represent different KG entities as low-dimensional vector embeddings via their neighborhood structure and then find counterpart entities by estimating the similarities between entity representations. However, most of the studies on the mainstream methods for entity alignment have paid little attention to abundant information on entity one-hop bidirectional neighbors and relational semantics centrality in KGs. Due to the heterogeneity of the knowledge graph, it is difficult to achieve satisfactory alignment results for entity alignment methods based on structure encoding. This paper investigates multilingual entity alignment strategies and proposes a novel relational semantics augmentation (RSA) model to alleviate these issues. RSA can fuse the entity’s bidirectional neighborhood path information and their connected relational semantics contexts. Comprehensive experiments on five benchmark EA datasets with the comparison of state-of-the-art entity alignment methods demonstrate the better performance of our RSA model.