NeoMaPy is based on a Temporal Markov Logic Networks (TMLN) model which extends the Markov Logic Networks (MLN) model with uncertain temporal facts and rules. Total and partial tem- poral (in)consistency relations between sets of temporal formulae are examined. We have proposed a new Temporal Parametric Semantics (TPS) which allows combining several sub-functions leading to different assessment strategies.
We have developed the new NeoMaPy tool (Github link) which computes the MAP inference on MLNs and TMLNs with several TPS. To enhance the graph visualisation, we used GraphStream.
This work has been published at IJCAI’23 (demo) and CIKM’23 (long paper). The work done in collaboration with Victor David (INRIA Sophia Antipolis) and Raphaël Fournier (CNAM). The work was funded by the ANR DAPHNE project.
>The NeoMaPy Framework – a demonstration for IJCAI23>>