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UID:140@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20250324T100000
DTEND;TZID=Europe/Paris:20250324T100000
DTSTAMP:20260602T130233Z
URL:https://isdm.umontpellier.fr/events/datalog-fact-explanation-using-gro
 up-sat-solver-2-2/
SUMMARY:Datalog Fact Explanation Using Group-SAT Solver
DESCRIPTION:Salle des conseils P Raynaud\, au 2ème étage\, du bâtiment 1
 1 dit le château\, 2 Place Pierre Viala Campus La Gaillarde\nSEmantic web
  SeminAr MontpEllier\nPierre BISQUERT (INRAE IATE)\n\nAbstract: One of the
  major benefits of symbolic AI is explainability. When new knowledge is ob
 tained 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.\n\nIn this talk\, we will 1) discuss th
 e notion(s) of explanation of a query in the context of a (Datalog) knowle
 dge base\, then 2) we will present a method to optimise the time used by t
 he SAT solver (by creating a hypergraph representing the grounded knowledg
 e 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 co
 ntext of InteGraal\, a tool for reasoning over heterogeneous and federated
  data sources.\n\nLabéliséHallesIA
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DTSTART:20241027T020000
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