NeoMaPy: a Parametric Framework for Reasoning with MAP Inference on Temporal Markov Logic Networks (IJCAI’23 & CIKM’23)

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.