Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs (Technical Report)
This addresses a key scalability issue for probabilistic logic programming in query answering over knowledge graphs, though it appears incremental as it builds on existing techniques.
The paper tackles the grounding bottleneck in probabilistic logic programming inference by proposing a Datalog-based approach that integrates knowledge compilation with forward reasoning on non-ground programs, eliminating this bottleneck for knowledge graph query answering while providing fast approximations on classical benchmarks.
State-of-the-art inference approaches in probabilistic logic programming typically start by computing the relevant ground program with respect to the queries of interest, and then use this program for probabilistic inference using knowledge compilation and weighted model counting. We propose an alternative approach that uses efficient Datalog techniques to integrate knowledge compilation with forward reasoning with a non-ground program. This effectively eliminates the grounding bottleneck that so far has prohibited the application of probabilistic logic programming in query answering scenarios over knowledge graphs, while also providing fast approximations on classical benchmarks in the field.