Integration of weighted knowledge bases
Artificial Intelligence
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Incomplete Statistical Information Fusion and Its Application to Clinical Trials Data
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
The Non-archimedean Polynomials and Merging of Stratified Knowledge Bases
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Event Composition with Imperfect Information for Bus Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Analyzing the degree of conflict among belief functions
Artificial Intelligence
Intelligent Sensor Information System For Public Transport - To Safely Go
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Event modelling and reasoning with uncertain information for distributed sensor networks
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
A framework for managing uncertain inputs: An axiomization of rewarding
International Journal of Approximate Reasoning
Measuring inconsistency in requirements specifications
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A Blame-Based Approach to Generating Proposals for Handling Inconsistency in Software Requirements
International Journal of Knowledge and Systems Science
| Hi-index | 0.00 |
Knowledge is an important component in many intelligent systems. Since items of knowledge in a knowledge base can be conflicting, especially if there are multiple sources contributing to the knowledge in this base, significant research efforts have been made on developing inconsistency measures for knowledge bases and on developing merging approaches. Most of these efforts start with flat knowledge bases. However, in many real-world applications, items of knowledge are not perceived with equal importance, rather, weights (which can be used to indicate the importance or priority) are associated with items of knowledge. Therefore, measuring the inconsistency of a knowledge base with weighted formulae as well as their merging is an important but difficult task. In this paper, we derive a numerical characteristic function from each knowledge base with weighted formulae, based on the Dempster-Shafer theory of evidence. Using these functions, we are able to measure the inconsistency of the knowledge base in a convenient and rational way, and are able to merge multiple knowledge bases with weighted formulae, even if knowledge in these bases may be inconsistent. Furthermore, by examining whether multiple knowledge bases are dependent or independent, they can be combined in different ways using their characteristic functions, which cannot be handled (or at least have never been considered) in classic knowledge based merging approaches in the literature.