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Communication Dans Un Congrès Année : 2023

NeoMaPy: A Parametric Framework for Reasoning with MAP Inference on Temporal Markov Logic Networks

Résumé

Reasoning on inconsistent and uncertain data is challenging, especially for Knowledge-Graphs (KG) to abide temporal consistency. Our goal is to enhance inference with more general time interval semantics that specify their validity, as regularly found in historical sciences. We propose a new Temporal Markov Logic Networks (TMLN) model which extends the Markov Logic Networks (MLN) model with uncertain temporal facts and rules. Total and partial temporal (in)consistency relations between sets of temporal formulae are examined. We then propose a new Temporal Parametric Semantics (TPS) which allows combining several sub-functions leading to different assessment strategies. Finally, we present the new NeoMaPy tool, to compute the MAP inference on MLNs and TMLNs with several TPS. We compare our performances with state-of-the-art inference tools and exhibit faster and higher quality results.

Dates et versions

hal-04347088 , version 1 (15-12-2023)

Identifiants

Citer

Victor David, Raphaël Fournier-S'Niehotta, Nicolas Travers. NeoMaPy: A Parametric Framework for Reasoning with MAP Inference on Temporal Markov Logic Networks. CIKM 2023 - International Conference on Information and Knowledge Management, Oct 2023, Birmingham (UK), United Kingdom. pp.400-409, ⟨10.1145/3583780.3614757⟩. ⟨hal-04347088⟩
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