A Characteristic Function Approach to Inconsistency Measures for Knowledge Bases.

Knowledge is an important component in many intelligent systems.<br/>Since items of knowledge in a knowledge base can be conflicting, especially if<br/>there are multiple sources contributing to the knowledge in this base, significant<br/>research efforts have been made on developing inconsistency measures for<br/>knowledge bases and on developing merging approaches. Most of these efforts<br/>start with flat knowledge bases. However, in many real-world applications, items<br/>of knowledge are not perceived with equal importance, rather, weights (which<br/>can be used to indicate the importance or priority) are associated with items of<br/>knowledge. Therefore, measuring the inconsistency of a knowledge base with<br/>weighted formulae as well as their merging is an important but difficult task. In<br/>this paper, we derive a numerical characteristic function from each knowledge<br/>base with weighted formulae, based on the Dempster-Shafer theory of evidence.<br/>Using these functions, we are able to measure the inconsistency of the knowledge<br/>base in a convenient and rational way, and are able to merge multiple knowledge<br/>bases with weighted formulae, even if knowledge in these bases may be<br/>inconsistent. Furthermore, by examining whether multiple knowledge bases are<br/>dependent or independent, they can be combined in different ways using their<br/>characteristic functions, which cannot be handled (or at least have never been<br/>considered) in classic knowledge based merging approaches in the literature.