TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs

arXiv:2402.12309v1 Announce Type: new
Abstract: 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 …

abstract arxiv change complexity cs.cl differentiable evolution general graph graphs information knowledge knowledge graphs notion rules temporal type