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 case-based reasoning research


An Analysis of Current Trends in CBR Research Using Multiview Clustering

AI Magazine

In this report we review the research themes covered in these papers and identify the topics that are active at the moment. The main mechanism for this analysis is a clustering of the research papers based on both cocitation links and text similarity. It is interesting to note that the core set of papers has attracted citations from almost three thousand papers outside the conference collection so it is clear that the CBR conferences are a subpart of a much larger whole. It is remarkable that the research themes revealed by this analysis do not map directly to the subtopics of CBR that might appear in a textbook. Instead they reflect the applications-oriented focus of CBR research, and cover the promising application areas and research challenges that are faced. Each year since 1993 there has been an international or European conference on CBR. Up to 2007, this conference series produced 672 papers in all. In this report we examine the research themes evident in these papers and identify the most active research topics in CBR. At the 2008 conference we presented an analysis of the research themes in CBR, based on an analysis of the cocitation links in the research literature (Greene et al. 2008). That analysis was based on the core set of 672 papers from the CBR conferences with cocitation data coming from a set of 3461 papers that cite these papers (details on how cocitation links are determined are given later in the article). While cocitation analysis has been proven to be very effective at uncovering relational structure in the research literature (White and Griffith 1981), it has the shortcoming that recent papers will have few cocitation links as papers citing pairs of papers in the core set (that is, the source of cocitation links) have not yet appeared. This issue is evident in the plot of citation counts shown in figure 1 and ultimately makes it impossible to recognize the influence of more recent papers.



Case-based reasoning and law EDWINA L. RISSLAND 1, KEVIN D. ASHLEY2 and L. KARL BRANTING3

AI Classics

The research pursued in the early 1980s by Rissland, Ashley, Branting, and Skalak explored the rich vein of case-based reasoning in the context of legal argument. Some of these seminal projects were presented in a special 1991 pair of issues of the International Journal of Man-Machine Studies (e.g., Ashley 1991; Branting, 1991; Rissland & Skalak, 1991). Ideas from these research projects lay the foundation of what is now termed interpretive CBR, that is, how to interpret new cases in light of past interpretations. This work has also influenced the community that develops formal models of argumentation and defeasible reasoning, and these models have in turn contributed more formal models to CBR (e.g., Bench-Capon & Sartor, 2003). The AI and law community continues to provide a rich tributary of ideas and techniques about CBR and for integrating it with other reasoning modalities in CBR hybrids, such as rule-based reasoning, heuristic search, and information retrieval.


An Analysis of Current Trends in CBR Research Using Multi-View Clustering

AI Magazine

The European Conference on Case-Based Reasoning (CBR) in 2008 marked 15 years of international and European CBR conferences where almost seven hundred research papers were published. In this report we review the research themes covered in these papers and identify the topics that are active at the moment. The main mechanism for this analysis is a clustering of the research papers based on both co-citation links and text similarity. It is interesting to note that the core set of papers has attracted citations from almost three thousand papers outside the conference collection so it is clear that the CBR conferences are a sub-part of a much larger whole. It is remarkable that the research themes revealed by this analysis do not map directly to the sub-topics of CBR that might appear in a textbook. Instead they reflect the applications-oriented focus of CBR research, and cover the promising application areas and research challenges that are faced.