A Case Representation and Similarity Measurement Model with Experience-Grounded Semantics

Abstract

Case-based reasoning heavily depends on the structure and content of the cases, and semantics is essential to effectively represent cases. In the field of structured case representation, most of the works regarding case representation and measurement of semantic similarity between cases are based on model-theoretic semantics and their extensions. The purpose of this study is to explore the potential of experienced-grounded semantics in case representation and semantic similarity measurement. The main contents in this study are as follows: (i) a case representation model based on experience-grounded semantic is proposed, (ii) a novel semantic similarity measurement method with multi-strategy reasoning is introduced, and (iii) a case-based reasoning software for urban firefighting field based on the proposed model is designed and implemented. Theoretically, compared with traditional structured case representation methods, the proposed model not only represents case in a fully formalized way, but also provides a novel metric for computing the strength of the semantic relationship between cases. The proposed model has been applied in an intelligent decision-support software for urban firefighting.