Multi-view Knowledge Graph Embedding for Entity Alignment

TL;DR

This paper introduces a unified multi-view embedding framework for entity alignment in knowledge graphs, leveraging entity names, relations, and attributes to improve accuracy and robustness over existing methods.

Contribution

It proposes a novel multi-view embedding approach that combines different entity features and cross-KG inference, significantly enhancing entity alignment performance.

Findings

Outperforms state-of-the-art embedding-based entity alignment methods

Multiple views and cross-KG inference contribute to accuracy improvements

Effective combination strategies enhance robustness and performance

Abstract

We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as attributes, for refinement. However, a vast of entity features are still unexplored or not equally treated together, which impairs the accuracy and robustness of embedding-based entity alignment. In this paper, we propose a novel framework that unifies multiple views of entities to learn embeddings for entity alignment. Specifically, we embed entities based on the views of entity names, relations and attributes, with several combination strategies. Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs. Our experiments on real-world datasets show that the proposed framework significantly outperforms the state-of-the-art embedding-based entity alignment methods. The selected views, cross-KG inference and combination strategies all contribute to the performance improvement.