Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs

Efthymia Tsamoura, Víctor Gutiérrez-Basulto, Angelika Kimmig

AAAI 2020 pp. 10284-10291

doi:10.1609/AAAI.V34I06.6591 /aaai/2020/tsamoura2020aaai-beyond/

Abstract

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.

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Cite

Text

Tsamoura et al. "Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I06.6591

Markdown

[Tsamoura et al. "Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/tsamoura2020aaai-beyond/) doi:10.1609/AAAI.V34I06.6591

BibTeX

@inproceedings{tsamoura2020aaai-beyond,
  title     = {{Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs}},
  author    = {Tsamoura, Efthymia and Gutiérrez-Basulto, Víctor and Kimmig, Angelika},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {10284-10291},
  doi       = {10.1609/AAAI.V34I06.6591},
  url       = {https://mlanthology.org/aaai/2020/tsamoura2020aaai-beyond/}
}