Exploring an Alternative Method for Handling Inconsistency in the Fusion of Expert and Learnt Information
This paper presents an approach to reasoning with learnt and expert information where inconsistencies are present. Information is represented as an uncertain taxonomical hierarchy where each class is a concept specification either defined by an expert or learnt from data. We show through simple examples how learnt information and uncertain expert knowledge can be represented and how conclusions can be reasoned from the fused hierarchy. This reasoning mechanism relies on a default assumption to rank conclusions based on the position of contributing information in the class hierarchy. We examine the aggregation function of the default reasoning process and propose improvements that result in more natural fusions of expert and learnt hierarchical information.
KeywordsExpert Knowledge Aggregation Function Information Fusion Probability Interval Support Interval
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