Learning temporal granularity with quadruplet networks for temporal knowledge graph completion.

Temporal Knowledge Graphs (TKGs) capture the dynamic nature of real-world facts by incorporating temporal dimensions that reflect their evolving states. These variations add complexity to the task of knowledge graph completion. Introducing temporal granularity can make the representation of facts more precise. In this paper, we propose Learning Temporal Granularity with Quadruplet Networks (LTGQ), which addresses the inherent heterogeneity of TKGs by embedding entities, relations, and timestamps into distinct specialized spaces. This differentiation enables a finer-grained capture of semantic information across the temporal knowledge graph. Specifically, LTGQ incorporates triaffine transformations to model high-order interactions between the elements of quadruples, such as entities, relations, and timestamps, in TKGs. Simultaneously, it leverages Dynamic Convolutional Neural Networks (DCNNs) to extract representations of latent spaces across different temporal granularities. By achieving more robust alignment between facts and their respective temporal contexts, LTGQ effectively improves the accuracy of temporal knowledge graph completion. The proposed model was validated on five public datasets, demonstrating significant improvements in TKG completion tasks, thereby confirming the effectiveness of our approach.