Markov logic: A language and algorithms for link mining

Abstract

Link mining problems are characterized by high complexity (since linked objects are not statistically independent) and uncertainty (since data is noisy and incomplete). Thus they necessitate a modeling language that is both probabilistic and relational. Markov logic provides this by attaching weights to formulas in first-order logic and viewing them as templates for features of Markov networks. Many link mining problems can be elegantly formulated and efficiently solved using Markov logic. Inference algorithms for Markov logic draw on ideas from satisfiability testing, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms are based on convex optimization, pseudo-likelihood, and inductive logic programming. Markov logic has been used successfully in a wide variety of link mining applications and is the basis of the open-source Alchemy system.