Markov Logic Networks with Numerical Constraints
Chekol Melisachew Wudage, Jakob Huber, Christian Meilicke, Heiner Stuckenschmidt · Frontiers in artificial intelligence and applications · 2016
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 DB-pedia, thus illustrating its practical use.