TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs

Understand

Compared with static knowledge graphs, temporal knowledge graphs (tKG), which can capture the evolution and change of information over time, are more realistic and general.

• However, due to the complexity that the notion of time introduces to the learning of the rules, an accurate graph reasoning, e.g., predicting new links between entities, is still a difficult problem.
• In this paper, we propose TILP, a differentiable framework for temporal logical rules learning.
• By designing a constrained random walk mechanism and the introduction of temporal operators, we ensure the efficiency of our model.