Cost-Based Semantics for Querying Inconsistent Weighted Knowledge Bases
This work addresses inconsistency handling in knowledge bases for AI and database systems, offering a quantitative approach that is incremental to existing methods.
The paper tackles the problem of querying inconsistent weighted knowledge bases by introducing a cost-based semantics that defines certain and possible answers based on interpretation costs, and provides a comprehensive complexity analysis for description logics between ELbot and ALCO.
In this paper, we explore a quantitative approach to querying inconsistent description logic knowledge bases. We consider weighted knowledge bases in which both axioms and assertions have (possibly infinite) weights, which are used to assign a cost to each interpretation based upon the axioms and assertions it violates. Two notions of certain and possible answer are defined by either considering interpretations whose cost does not exceed a given bound or restricting attention to optimal-cost interpretations. Our main contribution is a comprehensive analysis of the combined and data complexity of bounded cost satisfiability and certain and possible answer recognition, for description logics between ELbot and ALCO.