  We aim at improving reasoning on inconsistent and uncertain data. We focus on
knowledge-graph data, extended with time intervals to specify their validity,
as regularly found in historical sciences. We propose principles on semantics
for efficient Maximum A-Posteriori inference on the new Temporal Markov Logic
Networks (TMLN) which extend the Markov Logic Networks (MLN) by uncertain
temporal facts and rules. We examine total and partial temporal (in)consistency
relations between sets of temporal formulae. Then we propose a new Temporal
Parametric Semantics, which may combine several sub-functions, allowing to use
different assessment strategies. Finally, we expose the constraints that
semantics must respect to satisfy our principles.
