Aligning Multiple Knowledge Graphs in a Single Pass
This work addresses a novel problem in knowledge graph fusion for researchers and practitioners, offering a first solution to multi-KG alignment, though it is incremental in extending existing pair-based methods.
The paper tackles the problem of aligning multiple knowledge graphs (more than two) simultaneously, which had not been addressed before, and proposes MultiEA, a framework that achieves effective and efficient alignment in a single pass, as demonstrated on newly constructed real-world benchmark datasets.
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.