Séminaire SESAME du 24 mars 2025
10h00, salle des conseils P Raynaud, au 2ème étage, du bâtiment 11 dit le château, 2 Place Pierre Viala Campus La Gaillarde, 34000 Montpellier
Datalog Fact Explanation Using Group-SAT Solver par Pierre BISQUERT, INRAE IATE :
One of the major benefits of symbolic AI is explainability. When new knowledge is obtained via a reasoning process, it is possible to determine precisely the elements of the knowledge base that yield this knowledge. Typically, one would use a SAT solver to compute the explanations. However, SAT-solving is computationally expensive, and as the knowledge base grows, the time required increases exponentially.
In this talk, we will 1) discuss the notion(s) of explanation of a query in the context of a (Datalog) knowledge base, then 2) we will present a method to optimise the time used by the SAT solver (by creating a hypergraph representing the grounded knowledge base and pruning the nodes that are not reachable from the fact that we want to explain), and finally 3) we will see its implementation in the context of InteGraal, a tool for reasoning over heterogeneous and federated data sources.
Référence
Akira Charoensit, David Carral, Pierre Bisquert, Lucas Rouquette, Federico Ulliana. Rule-aware Datalog Fact Explanation Using Group-SAT Solver. RuleML+RR 2024 - 8th International Joint Conference on Rules and Reasoning, Sep 2024, Bucarest, Romania. https://hal.science/hal-04706324