Probabilistic knowledge graphs (PKGs) improve traditional knowledge graphs by characterizing the inherent uncertainty of knowledge. In PKGs, each triple$(e_{1}, r,e_{2})$, representing that entity$e_{1}$has a relationship$r$with entity$e_{2}$, is associated with a probability$\phi$indicating its likelihood of being true. PKGs have gained popularity in various domains, such as ProBase [22] and NELL [13].

Temporal Knowledge Graphs (TKGs), which record the evolution of relationships among entities over time, have been increasingly used in a myriad of applications. Despite their growing importance, the challenge of aligning entities in these evolving structures has yet to be satisfactorily addressed. Most existing techniques struggle to keep pace with the continual stream of new entities and relations, which is a defining characteristic of TKGs. In response to this challenge, we propose a novel teacher-student approach for incremental entity alignment in evolving TKGs. Our solution leverages a Graph Attention Network (GAT) as the teacher model and a sampling Graph Convolutional Network (GCN) as a lightweight, adaptable student model. This approach efficiently navigates the evolving complexities inherent in TKGs, leading to remarkable improvements in the efficiency and effectiveness of entity alignment. The experimental results substantiate the superior performance of our approach in achieving effective entity alignment promptly, outstripping existing state-of-the-art models. As such, our study contributes a crucial step towards efficiently handling evolving entity alignment tasks in TKGs.

Abstract Entity alignment is an essential process in knowledge graph (KG) fusion, which aims to link entities representing the same real-world object in different KGs, to achieve entity expansion and graph fusion. Recently, embedding-based entity pair similarity evaluation has become mainstream in entity alignment research. However, these methods heavily rely on labelled entity pairs, which are often unavailable. Some self-supervised methods exploit features of KGs regardless of noise when generating aligned entity pairs. To resolve this issue, we propose a generative adversarial entity alignment method, which is more robust to noise data. The proposed method then exploits both attribute and structure information in the KGs and applies a BERT-based contrastive loss function to embed entities in KGs. Experimental results on several benchmark datasets demonstrate the superiority of our framework compared with most existing state-of-the-art entity alignment methods.