Post-Pruning vs. Early Stopping: Which Works Better for Link Prediction with AnyBurl?

Overview & Synopsis

Rule-based link prediction combines symbolic and statistical artificial intelligence methods by inductively learning a set of rules from a knowledge graph (KG), with the aim of completing the graph when a query cannot be answered from the KG alone. Several systems have been proposed, and while they perform well on classical benchmarks, even compared with embedding-based systems, many of them produce a large number of rules. This compromises scalability in the query-answering phase, especially under strong hardware constraints. In this work, we investigate the behavior of AnyBurl, a state-of-the-art rule learner for link prediction, to determine whether running it for a given time and then pruning the learned rules yields better query-answering performance at test time than a simpler approach that stops rule generation earlier.