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State-of-the-art inference approaches in probabilistic logic programmingtypically start by computing the relevant ground program with respect to thequeries of interest, and then use this program for probabilistic inferenceusing knowledge compilation and weighted model counting. We propose analternative approach that uses efficient Datalog techniques to integrateknowledge compilation with forward reasoning with a non-ground program. Thiseffectively eliminates the grounding bottleneck that so far has prohibited theapplication of probabilistic logic programming in query answering scenariosover knowledge graphs, while also providing fast approximations on classicalbenchmarks in the field.

Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs (Technical Report)