  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.
