Conference Papers Year : 2023

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

Abstract

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.

Domains

Artificial Intelligence [cs.AI] Databases [cs.DB]

Nicolas Travers  : Connect in order to contact the contributor

https://hal.science/hal-04347093

Submitted on : Friday, December 15, 2023-2:09:03 PM

Last modification on : Friday, July 19, 2024-11:38:05 AM

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Dates and versions

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

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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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