Unsupervised fuzzy temporal knowledge graph entity alignment via joint fuzzy semantics learning and global structure learning

Abstract

Temporal Knowledge Graph Entity Alignment (TKGEA) aims to identify the equivalent entities between different Temporal Knowledge Graphs (TKGs), which is important to knowledge fusion. The current mainstream TKGEA models are supervised embedding-based models that rely on pre-aligned seeds and implicitly encode structural information into entity embedding space for identifying equivalent entities. To deal with the TKGs structural information, some models use Graph Neural Network (GNN) encoding. But they ignore the design of decoders, failing to fully leverage the TKGs structural information. In addition, they primarily focus on crisp TKGs with clear entity semantics. However, many real-world TKGs exhibit fuzzy semantics. This fuzzy information makes existing TKGEA models face the challenge of handling the fuzzy semantics when aligning the equivalent fuzzy entities. To solve the above problems, we propose a novel unsupervised Fuzzy Temporal Knowledge Graphs Entity Alignment (EA) framework that jointly performs Fuzzy Semantics Learning and Global Structure Learning, namely FTFS. In this framework, we convert the EA task into an unsupervised optimal transport task between two intra-graph matrices, eliminating the necessity for pre-aligned seeds and thereby avoiding intensive labor. Since we further consider the relation between graph structure and entities during the optimal-transport-based decoder module, it can make better use of the global structural information rather than simply encoding it implicitly into the embedding space. Moreover, unlike TKGEA models, which use binary classification to represent temporal relational facts, we introduce fuzzy semantics learning to embed membership degrees of fuzzy temporal relational facts. Extensive experiments on five FTKG datasets show that our unsupervised method is superior to the state-of-the-art EA methods.