Markov Logic : An Interface Layer for Artificial Intelligence

Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system. Table of Contents: Introduction / Markov Logic / Inference / Learning / Extensions / Applications / Conclusion Introduction -- The interface layer -- What is the interface layer for AI -- Markov logic and alchemy: an emerging solution -- Overview of the book -- Markov logic -- First-order logic -- Markov networks -- Markov logic -- Relation to other approaches -- Inference -- Inferring the most probable explanation -- Computing conditional probabilities -- Lazy inference -- Lifted inference -- Learning -- Weight learning -- Structure learning and theory revision -- Unsupervised learning -- Transfer learning -- Extensions -- Continuous domains -- Infinite domains -- Recursive Markov logic -- Relational decision theory -- Applications -- Collective classification -- Social network analysis and link prediction -- Entity resolution -- Information extraction -- Unsupervised coreference resolution -- Robot mapping -- Link-based clustering -- Semantic network extraction from text -- Conclusion -- The alchemy system -- Input files -- Inference -- Weight learning -- Structure learning -- Bibliography -- Biography