NeoMaPy: A Framework for Computing MAP Inference on Temporal Knowledge Graphs - Archive ouverte HAL
Communication Dans Un Congrès Année : 2023

NeoMaPy: A Framework for Computing MAP Inference on Temporal Knowledge Graphs

Résumé

Markov Logic Networks (MLN) are used for reasoning on uncertain and inconsistent temporal data. We proposed the TMLN (Temporal Markov Logic Network) which extends them with sorts/types, weights on rules and facts, and various temporal consistencies. The NeoMaPy framework integrates it as a knowledge graph based on conflict graphs which offers flexibility for reasoning with parametric Maximum A Posteriori (MAP) inferences, efficiency with an optimistic heuristic and interactive graph visualization for results explanation.

Domaines

Intelligence artificielle [cs.AI] Base de données [cs.DB]

Nicolas Travers  : Connectez-vous pour contacter le contributeur

https://hal.science/hal-04347093

Soumis le : vendredi 15 décembre 2023-14:09:03

Dernière modification le : vendredi 19 juillet 2024-11:38:05

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Dates et versions

hal-04347093 , version 1 (15-12-2023)

Identifiants

Citer

Victor David, Raphael Fournier-S'Niehotta, Nicolas Travers. NeoMaPy: A Framework for Computing MAP Inference on Temporal Knowledge Graphs. IJCAI-23 - Thirty-Second International Joint Conference on Artificial Intelligence, Aug 2023, Macau, China. ⟨10.24963/ijcai.2023/831⟩. ⟨hal-04347093⟩

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