Event entity alignment for multi-source encyclopedia knowledge bases with the similarity of event element sets

The key to constructing an event knowledge graph is to acquire event knowledge. At present, the method of event extraction from text is not accurate enough, while the event information obtained through encyclopedia knowledge bases has the advantages of high accuracy, good structure and rich multimedia resources. However, acquiring event entities from a single encyclopedia knowledge base has the problem of missing information, so the fusion technology for multisource encyclopedia knowledge bases is needed urgently, in which entity alignment is the core technology. Aiming at the shortcomings of current alignment methods focusing on static entity in event entity alignment, we propose an event entity alignment method based on event elements, which calculates entity similarity according to multiple event elements. In contrast to the algorithm based on latent Dirichlet allocation (LDA) model and the method based on representation learning using BERT, the proposed method provides a significant performance improvement in event entity alignment. Especially, the method optimizes the threshold setting so that it enhances the ability to identify the presence of aligned entities.