Benchmarking Challenges for Temporal Knowledge Graph Alignment

Temporal knowledge graph alignment (TKGA) discovers the equivalent elements among heterogeneous temporal knowledge graphs (TKGs), and thus can increase the coverage of a given TKG. However, existing TKGA datasets fail to mirror the real-life challenges, and the oversimplified scenarios may even impede the fair comparison and development of the alignment solutions. To address the aforementioned issues, in this work, we propose to benchmark challenges for temporal knowledge graph alignment by establishing a new dataset, i.e., BETA, which features multi-granular temporal information, more realistic quadruple distribution, and new challenging alignment scenarios. Furthermore, we also offer a simple yet effective solution, MGTEA, to address the aforementioned challenges, which effectively models the complex structural and multi-granular temporal features to facilitate the alignment. Extensive experiments reveal that BETA indeed better mirrors the real-life challenges, and there is still room for developing more advanced solutions to address these difficulties, despite of the superior performance achieved by MGTEA.