TILP: Differentiable Learning of Temporal Logical Rules on Knowledge
  Graphs
Siheng Xiong Yuan Yang Faramarz Fekri James Clayton Kerce
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. We present
temporal features modeling in tKG, e.g., recurrence, temporal order, interval
between pair of relations, and duration, and incorporate it into our learning
process. We compare TILP with state-of-the-art methods on two benchmark
datasets. We show that our proposed framework can improve upon the performance
of baseline methods while providing interpretable results. In particular, we
consider various scenarios in which training samples are limited, data is
biased, and the time range between training and inference are different. In all
these cases, TILP works much better than the state-of-the-art methods.