Paper: Implementing Weighted Abduction in Markov Logic

ACL ID W11-0107
Title Implementing Weighted Abduction in Markov Logic
Venue IWCS
Session Main Conference
Year 2011
Authors

Abduction is a method for finding the best explanation for observations. Arguably the most advanced approach to abduction, especially for natural language processing, is weighted abduction, which uses logical formulas with costs to guide inference. But it has no clear probabilistic semantics. In this paper we propose an approach that imple- ments weighted abduction in Markov logic, which uses weighted first-order formulas to represent probabilistic knowledge, pointing toward a sound probabilistic semantics for weighted abduction. Application to a series of challenge problems shows the power and coverage of our approach.