MV-Datalog+/-: Effective Rule-Based Reasoning with Uncertain Observations (Extended Abstract)

Matthias Lanzinger, Stefano Sferrazza, Georg Gottlob

IJCAI 2023 pp. 6447-6451

doi:10.24963/IJCAI.2023/718 /ijcai/2023/lanzinger2023ijcai-mv/

Abstract

Modern data processing applications often combine information from a variety of complex sources. Oftentimes, some of these sources, like Machine-Learning systems or crowd-sourced data, are not strictly binary but associated with some degree of confidence in the observation. Ideally, reasoning over such data should take this additional information into account as much as possible. To this end, we propose extensions of Datalog and Datalog+/- to the semantics of Lukasiewicz logic Ł, one of the most common fuzzy logics. We show that such an extension preserves important properties from the classical case and how these properties can lead to efficient reasoning procedures for these new languages.

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Cite

Text

Lanzinger et al. "MV-Datalog+/-: Effective Rule-Based Reasoning with Uncertain Observations (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/718

Markdown

[Lanzinger et al. "MV-Datalog+/-: Effective Rule-Based Reasoning with Uncertain Observations (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/lanzinger2023ijcai-mv/) doi:10.24963/IJCAI.2023/718

BibTeX

@inproceedings{lanzinger2023ijcai-mv,
  title     = {{MV-Datalog+/-: Effective Rule-Based Reasoning with Uncertain Observations (Extended Abstract)}},
  author    = {Lanzinger, Matthias and Sferrazza, Stefano and Gottlob, Georg},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {6447-6451},
  doi       = {10.24963/IJCAI.2023/718},
  url       = {https://mlanthology.org/ijcai/2023/lanzinger2023ijcai-mv/}
}