Knowledge Engineering with Markov Logic Networks: A Review

Within the realm of statistical relational knowledge representation formalisms, Markov logic is perhaps one of the most flexible and general languages, for it generalises both first-order logic (for finite domains) and probabilistic graphical models. Knowledge engineering with Markov logic is, however, not a straightforward task. In particular, modelling approaches that are too firmly rooted in the principles of logic often tend to produce unexpected results in practice. In this paper, I collect a number of issues that are relevant to knowledge engineering practice: I describe the fundamental semantics of Markov logic networks and explain how simple probabilistic properties can be represented. Furthermore, I discuss fallacious modelling assumptions and summarise conditions under which generalisation across domains may fail. As a collection of fundamental insights, the paper is primarily directed at knowledge engineers who are new to Markov logic.