Many interesting research problems, such as ontology alignment and collective classification, require probabilistic and collective inference over imprecise evidence. Existing approaches are typically ad-hoc and problemspecific, requiring significant effort to devise and provide poor generalizability. In this paper, we introduce probabilistic similarity logic (PSL), a simple, yet powerful language for describing problems which require probabilistic reasoning about similarity where, in addition to reasoning probabilistically, we want to capture both logical constraints and imprecision. We prove that PSL inference is polynomial and outline a wide range of application areas for PSL. 1
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