|Data quality is a crucial aspect of case-based reasoning (CBR), and incomplete data is a ubiquitous challenge that can significantly affect the accuracy and effectiveness of CBR systems. Incompleteness arises when a case lacks relevant information needed to solve a problem. Existing CBR systems often struggle to handle such cases, leading to sub-optimal solutions, and making it challenging to apply CBR in real-world settings. This paper highlights the importance of data quality in CBR and emphasizes the need for systems to handle incomplete data effectively. The authors provide for the first time a framework for addressing the issue of incompleteness under the open-world assumption. The proposed approach leverages a combination of data-driven and knowledge-based techniques to detect incompleteness. The approach offers a promising solution to the incompleteness dimension of data quality in CBR and has the potential to improve the practical utility of CBR systems in various domains as illustrated by the results of a real data-based evaluation.
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