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.

中文翻译：

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具有基于经验的语义的案例表示和相似性测量模型

基于案例的推理在很大程度上取决于案例的结构和内容，而语义对于有效地表示案例至关重要。在结构化案例表示领域，关于案例表示和案例间语义相似度度量的大部分工作都是基于模型理论语义及其扩展。本研究的目的是探索基于经验的语义在案例表示和语义相似性测量中的潜力。本研究的主要内容如下：（i）提出了一种基于经验的语义的案例表示模型，（ii）引入了一种新的多策略推理的语义相似度测量方法，以及（iii）一个案例-设计并实现了基于该模型的城市消防领域推理软件。从理论上讲，与传统的结构化案例表示方法相比，该模型不仅以完全形式化的方式表示案例，而且为计算案例之间的语义关系强度提供了一种新的度量。该模型已应用于城市消防智能决策支持软件。