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Logical rules are a popular knowledge representation language in manydomains, representing background knowledge and encoding information that can bederived from given facts in a compact form. However, rule formulation is acomplex process that requires deep domain expertise,and is further challengedby today's often large, heterogeneous, and incomplete knowledge graphs. Severalapproaches for learning rules automatically, given a set of input examplefacts,have been proposed over time, including, more recently, neural systems.Yet, the area is missing adequate datasets and evaluation approaches: existingdatasets often resemble toy examples that neither cover the various kinds ofdependencies between rules nor allow for testing scalability. We present a toolfor generating different kinds of datasets and for evaluating rule learningsystems, including new performance measures.

RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools