Entity alignment for temporal knowledge graphs via adaptive graph networks

The temporal entity alignment task aims to discover entities with the same meaning but belonging to different temporal knowledge graphs (KGs). Most existing entity alignment studies mainly focus on static entity alignment, while temporal entity alignment has not received enough attention. However, entity alignment containing temporal information is more in line with real-world application scenarios, and applying static entity alignment models directly to temporal KGs usually does not achieve satisfactory performance because many events (entities) in the knowledge graph will change with time. Therefore, we propose an adaptive graph network (AGN) for entity alignment between temporal KGs. Specifically, we use a time-aware graph attention network model as an encoder to aggregate the features and temporal relationships of neighboring nodes. To adapt to various temporal knowledge graphs, we design a training scheme with adaptive relative error loss minimization, which aims to provide relative positions of entities in vector space for model optimization. Furthermore, we propose an adaptive fine-tuning distance algorithm based on supervised information, which aims to adaptively fine-tune the locations of entities in the vector space for the entity alignment similarity measure. Our proposed AGN model can be naturally extended to entity alignment datasets across multiple temporal knowledge graphs. We evaluate our proposed model via temporal knowledge graphs on public datasets and our newly proposed noisy dataset. We also demonstrate the advantages of the AGN model through extensive experiments, which achieves state-of-the-art performance on the temporal knowledge graph dataset.