Information Retrieval, Imaging and Probabilistic Logic

Imaging is a class of non-Bayesian methods for the revision of probability density functions originally proposed as a semantics for conditional logic. Two of these revision functions, standard imaging and general imaging, have successfully been applied to modelling information retrieval by Crestani and van Rijsbergen. Due to the problematic nature of a\direct" implementation of imaging revision functions, in this paper we propose their alternative implementation by representing the semantic structure that underlies imaging-based conditional logics in the language of a probabilistic (Bayesian) logic. Besides showing the potential of this \Bayesian" tool for the representation of non-Bayesian revision functions, recasting these models of information retrieval in such a general purpose knowledge representation and reasoning tool paves the way to a possible integration of these models with other more KR-oriented models of IR, and to the exploitation of generalpurpose domain-knowledge.