Literature and Tools

Markov Logic is a probabilistic logic which combines the ideas
of Markov networks with those of first-order logic. It is one of the many languages that falls into the realm of statistical relational learning (SRL). SRL is concerned with models of domains that exhibit both uncertainty and relational structure.

Markov logic originates in the 2006 paper by Richardson and Domingos.

Some of the most successful applications can be found in the semantic web and natural language processing area:

• Natural Language Processing: Multilingual Semantic Role Labelling with Markov Logic, Ivan Meza Ruiz, Sebastian Riedel, CoNLL 2009
• Ontology refinement: Automatically Refining the Wikipedia Infobox Ontology, Fei Wu and Daniel S. Weld, WWW2008
• Ontology Matching: A Probabilistic-Logical Framework for Ontology Matching, Mathias Niepert, Christian Meilicke, Heiner Stuckenschmidt, AAAI 2010

Some open-source Markov Logic tools are available. We will mostly use TheBeast for in-class demos and experiments:

• Alchemy
• TheBeast

The Alchemy group at the University of Washington also maintains a list of Markov logic related publications.