Abstract
This study presents a case-based reasoning (CBR) system that makes use of general domain knowledge - referred to as a knowledge-intensive CBR system. The system applies a Bayesian analysis aimed at increasing the accuracy of the similarity assessment. The idea is to employ the Bayesian posterior distribution for each case symptom to modify the case descriptions and the dependencies in the model. To evaluate the system, referred to as BNCreek, two experiment sets are set up from a “food” and an “oil well drilling” application domain. In both of the experiments, the BNCreek is evaluated against two corresponding systems named TrollCreek and myCBR with Normalized Discounted Cumulative Gain (NDCG) and interpolated average Precision-Recall as the evaluation measures. The obtained results reveal the capability of Bayesian analysis to increase the accuracy of the similarity assessment.
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Acknowledgement
The authors would like to thank Prof. Paal Skalle for preparing drilling cases and Prof. Helge Langseth and Dr. Frode Sørmo for their useful suggestions.
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Nikpour, H., Aamodt, A., Bach, K. (2018). Bayesian-Supported Retrieval in BNCreek: A Knowledge-Intensive Case-Based Reasoning System. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_22
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DOI: https://doi.org/10.1007/978-3-030-01081-2_22
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