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.
Cite
Text
David et al. "NeoMaPy: A Framework for Computing MAP Inference on Temporal Knowledge Graphs." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/831Markdown
[David et al. "NeoMaPy: A Framework for Computing MAP Inference on Temporal Knowledge Graphs." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/david2023ijcai-neomapy/) doi:10.24963/IJCAI.2023/831BibTeX
@inproceedings{david2023ijcai-neomapy,
title = {{NeoMaPy: A Framework for Computing MAP Inference on Temporal Knowledge Graphs}},
author = {David, Victor and Fournier-S'niehotta, Raphaël and Travers, Nicolas},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2023},
pages = {7123-7126},
doi = {10.24963/IJCAI.2023/831},
url = {https://mlanthology.org/ijcai/2023/david2023ijcai-neomapy/}
}