Aligning Multiple Knowledge Graphs in a Single Pass

TL;DR

This paper introduces MultiEA, a novel framework for aligning multiple knowledge graphs simultaneously, utilizing shared embeddings and high-order similarity techniques, validated on new real-world benchmarks.

Contribution

It is the first to address multi-knowledge graph alignment in a single pass, proposing a shared encoder, three alignment strategies, and an inference enhancement method.

Findings

MultiEA effectively aligns multiple KGs in one pass.

The method outperforms existing pairwise alignment approaches.

New benchmark datasets demonstrate its efficiency and accuracy.

Abstract

Entity alignment (EA) is to identify equivalent entities across different knowledge graphs (KGs), which can help fuse these KGs into a more comprehensive one. Previous EA methods mainly focus on aligning a pair of KGs, and to the best of our knowledge, no existing EA method considers aligning multiple (more than two) KGs. To fill this research gap, in this work, we study a novel problem of aligning multiple KGs and propose an effective framework named MultiEA to solve the problem. First, we embed the entities of all the candidate KGs into a common feature space by a shared KG encoder. Then, we explore three alignment strategies to minimize the distances among pre-aligned entities. In particular, we propose an innovative inference enhancement technique to improve the alignment performance by incorporating high-order similarities. Finally, to verify the effectiveness of MultiEA, we construct two new real-world benchmark datasets and conduct extensive experiments on them. The results show that our MultiEA can effectively and efficiently align multiple KGs in a single pass. We release the source codes of MultiEA at: https://github.com/kepsail/MultiEA.