However, most existing systems only use simple keyword matching to identify relevant documents, resulting in unsatisfactory system performances. Recent approaches to IR dig into the inference process in order to solve this problem. Most of them are investigated within a probabilistic framework. In this paper, we consider the fuzzy modal logic framework in the definition of our inferential model. A document description is associated to a fuzzy world.
Inference is based on the fuzzy accessibility relations between worlds.
Due to the flexibility of the fuzzy logic framework, human-defined knowledge may be incorporated into our system. We report our experiments on a test corpus using a general manual thesaurus. It is shown that human-defined knowledge, when adapted to the application area and used adequately, leads to great improvements in system's performances. E. Birnbaum has reopened an important problem of the informational theory by a formulation of the informational-causal chain. General informational theory can substantially concern this particular problem, that is, studying the decomposition possibilities of formula alpha models beta and its circular, particularly, metaphysical case alpha models alpha.