MTLearn: Extracting Temporal Rules Using Datalog Rule Learners

We propose a framework for temporal rule learning from datasets, which capitalises on the availability of increasingly mature Datalog rule learners. Our approach is based on the idea of splitting a temporal dataset into windows, extracting static rules from each window with an off-the-shelf Datalog rule learner, and then combining the obtained static rules into temporal rules corresponding to the whole dataset. Temporal rules generated by our approach are expressed in DatalogMTL and are assigned time-sensitive confidence scores. We have implemented our approach in a system MTLearn compatible with any Datalog rule learner, as well as with a range of strategies for scoring the output temporal rules. The evaluation results on the task of temporal link prediction show that our proposed approach is highly competitive, achieve performance comparable to that of state-of-the-art machine learning models for both the extrapolation and the interpolation settings, while at the same time providing interpretable results.