Markov Logic Networks with Numerical Constraints

Markov logic networks (MLNs) have proven to be useful tools for reasoning about uncertainty in complex knowledge bases. In this paper, we extend MLNs with numerical constraints and present an efficient implementation in terms of a cutting plane method. This extension is useful for reasoning over uncertain temporal data. To show the applicability of this extension, we enrich log-linear description logics (DLs) with concrete domains (datatypes). Thereby, allowing to reason over weighted DLs with datatypes. Moreover, we use the resulting formalism to reason about temporal assertions in DBpedia, thus illustrating its practical use.