Temporal Knowledge Graph Entity Alignment via Representation Learning

Abstract

Entity alignment aims to construct a complete knowledge graph (KG) by matching the same entities in multi-source KGs. Existing methods mainly focused on the static KG, which assumes that the relationship between entities is permanent. However, almost every KG will evolve over time in practical applications, resulting in the need for entity alignment between such temporal knowledge graphs (TKGs). In this paper, we propose a novel entity alignment framework suitable for TKGs, namely Tem-EA. To incorporate temporal information, we use recurrent neural networks to learn temporal sequence representations. Furthermore, we use graph convolutional network (GCN) and translation-based embedding model to fully learn structural information representation and attribute information representation. Based on these two representations, the entity similarity is calculated separately and combined using linear weighting. To improve the accuracy of entity alignment, we also propose a concept of nearest neighbor matching, which matches the most similar entity pair according to distance matrix. Experiments show that our proposed model has a significant improvement compared to previous methods.