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

Victor David; Raphael Fournier-S'niehotta; Nicolas Travers

IJCAI 2023poster2 citations

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

Victor David, Raphael Fournier-S'niehotta, Nicolas Travers

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.

Knowledge Representation and Reasoning: KRR: Reasoning about knowledge and beliefKnowledge Representation and Reasoning: KRR: ApplicationsMultidisciplinary Topics and Applications: MDA: DatabasesPlanning and Scheduling: PS: Markov decisions processes

BibTeX

@inproceedings{ijcai2023p831,
  title     = {NeoMaPy: A Framework for Computing MAP Inference on Temporal Knowledge Graphs},
  author    = {David, Victor and Fournier-S'niehotta, Raphael and Travers, Nicolas},
  booktitle = {Proceedings of the Thirty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-23}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Edith Elkind},
  pages     = {7123--7126},
  year      = {2023},
  month     = {8},
  note      = {Demo Track},
  doi       = {10.24963/ijcai.2023/831},
  url       = {https://doi.org/10.24963/ijcai.2023/831},
}