Abstract
Case Based Reasoning (CBR) is an important technique in artificial intelligence, which has been applied to various kinds of problems in a wide range of domains. Selecting case representation formalism is critical for the proper operation of the overall CBR system. In this paper, we survey and evaluate all of the existing case representation methodologies. Moreover, the case retrieval and future challenges for effective CBR are explained. Case representation methods are grouped in to knowledge-intensive approaches and traditional approaches. The first group overweight the second one. The first methods depend on ontology and enhance all CBR processes including case representation, retrieval, storage, and adaptation. By using a proposed set of qualitative metrics, the existing methods based on ontology for case representation are studied and evaluated in details. All these systems have limitations. No approach exceeds 53% of the specified metrics. The results of the survey explain the current limitations of CBR systems. It shows that ontology usage in case representation needs improvements to achieve semantic representation and semantic retrieval in CBR system.
Highlights
Clinical Decision Support System (CDSS) that bear similarities with human reasoning and explanation have benefits
Case-Based Reasoning (CBR) is a promising AI method that can be applied as ―reasoning by experience in AI‖ for implementing CDSSs in the medical domain since it learns from experience in order to solve a current situation [6]
A CBR system should be organized with some basic elements: the knowledge representation, to depict the cases, and the similarity measure to define how much a case is similar to another one
Summary
Clinical Decision Support System (CDSS) that bear similarities with human reasoning and explanation have benefits. They improve case indexing and retrieval, case representation and storage in case base, case adaptation and case retention They solve the problem of knowledge acquisition bottleneck by allowing the case base to www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 6, No 11, 2015 be represented as ontology and allowing discovery of cases from existing domain ontologies [15, 16]. Cordier proposed a model [12] that composed of five stages: (1) preparation, (2) memory retrieval, (3) reuse (adaptation), (4) revision, and (5) memorization (learning) This model asserts a case base building step, preparation, where a set of cases is capitalized in the knowledge base (base case).
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