The Form and the Content: Non-Monotonic Reasoning with Syntactic Contextual Filtering
Abstract
In order to avoid ambiguity and be efficient, the context in which a query is made can help to better target the relevant pieces of information from the knowledge base to be processed by the inference system. In this paper, we are interested in the notion of "dynamical compartmentalization" where the knowledge base that will be used for reasoning is dynamically extracted from the original base. Compartmentalization is a selection of a sub-base which is done according to a function, called "refiner", and depending on this function some properties are satisfied. We introduce a particular "syntactic refiner" that uses a similarity symbol-based distance between a context (a multiset of variable symbols) and a formula of a knowledge base. We prove that the inference operator based on this refiner, called "contextual inference", satisfies a series of desirable axioms
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