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

arXiv: https://arxiv.org/abs/1911.07750 · HTML: https://mlsanity.com/paper/1911.07750/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [Materializing Knowledge Bases via Trigger Graphs](https://mlsanity.com/paper/2102.02753/) (2021)
- [Efficient and Sound Probabilistic Verification for AI Agents](https://mlsanity.com/paper/2606.20510/) (2026)
- [Quantitative and Stream Extensions of Answer Set Programming](https://mlsanity.com/paper/2109.08281/) (2021)
- [A Scalable Technique for Weak-Supervised Learning with Domain Constraints](https://mlsanity.com/paper/2301.05253/) (2023)
- [Bottom-Up Grounding in the Probabilistic Logic Programming System Fusemate](https://mlsanity.com/paper/2305.18924/) (2023)
- [Automated Hybrid Grounding Using Structural and Data-Driven Heuristics](https://mlsanity.com/paper/2507.17493/) (2025)

## Abstract (arXiv)

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.