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  • × theme_ss:"Case Based Reasoning"
  1. Akerele, O.; David, A.; Osofisan, A.: Using the concepts of Case Based Reasoning and Basic Categories for enhancing adaptation to the user's level of knowledge in Decision Support System (2014) 0.04 
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    Abstract
    In most search systems, mapping queries with documents employs techniques such as vector space model, naïve Bayes, Bayesian theorem etc. to classify resulting documents. In this research studies, we are proposing the use of the concept of basic categories to representing the user's level of knowledge based on the concepts he employed during his search activities, so that the system could propose adapted results based on the observed user's level of knowledge. Our hypothesis is that this approach will enhance the decision support system for solving decisional problems in which information retrieval constitutes the backbone technical problem.
    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  2. Mahapatra, R.; Sen, A.: Case base management systems : providing database support to case-based reasoners (1994) 0.02 
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    Abstract
    Case based reasoning offers a new approach for developing knowledge based systems. Most systems are currently prototypes. A number of research issues need to be resolved to facilitate the transition of these prototypes to large application systems, the primary issue being to develop data management support for these prototypes. Analyzes this data management support and proposes a new concept called a casease management system to perfom data management functions for case based systems
  3. Aamodt, A.; Plaza, E.: Case-based reasoning : foundation issues, methodological variations, and systems approaches (1994) 0.02 
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    Abstract
    Gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems
  4. Veleso, M.; Munoz-Avila, H.; Bergmann, R.: Case-based planning : selected methods and systems (1996) 0.02 
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    Abstract
    Describes a framework for case based planning based on the case based reasoning process model. It covers work based on the integration of a generative problem solver with a case based component. Describes case based reasoning planning systems developed at the Carnegie Mellon University, Pittsburgh
  5. Dearden, A.M.; Harrison, M.D.: Abstract models for HCI (1997) 0.02 
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    Abstract
    Investigates the use of formal mathematical models in the design of interactive systems and argues for the development of generic models that describe the behaviour of a class of interactive systems. It is possible to construct a generic model of a class of interactive systems at an intermediate level of abstraction. Such a model would offer wider reusability than detailed specifications of a single system, but greater expressiveness and support for software development than fully generate abstract models. Reviews a number of existing models in the literature and presents a generic model of interactive case memories, a class of systems used in case-based reasoning
  6. Golding, A.R.; Rosenbloom, P.S.: Improving accuracy by combining rule-based and case-based reasoning (1996) 0.01 
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    Date
    6. 3.1997 16:22:15
  7. Ram, A.; Santamaria, J.C.: Continuous case-based reasoning (1997) 0.01 
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    Date
    6. 3.1997 16:22:15
  8. Kohno, T.: Error repair and knowledge acquisition via case-based reasoning (1997) 0.01 
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    Date
    6. 3.1997 16:22:15
  9. Mazzucchelli, A.; Sartori , F.: String similarity in CBR platforms : a preliminary study (2014) 0.01 
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    Pages
    S.22-29
  10. Kolodner, J.: Case-based reasoning (1993) 0.01 
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    Content
    Pt.1: Background: waht is CBR? Case studies of several case-based reasoners. Reasoning using cases. The cognitive model. Pt.2: The case library: representing and indexing cases. Indexing vocabulary. Methods for index selection. Pt.3: Retrieving cases from the case library. Organizational structures and retrieval algorithms. Matching and ranking cases. Indexing and retrieval. Pt.4: Using cases. Adaptation methods and strategies. Controlling adaptation. Using cases for interpretation and evaluation. Pt.5: Pulling it all together. Building a case-based reasoner. Conclusions, opportunities, challenges. Appendix: A case library of case-based reasoning systems
  11. Ozturk, P.; Aamodt, A.: ¬A context model for knowledge-intensive case-based reasoning (1998) 0.01 
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    Abstract
    Reports on research which studied how the incorporation of case-specific, episodic, knowledge enables decision-support systems to become more robust and to adapt to a changing environment by continuously retaining new problem-solving cases as they occur during normal system operation
  12. Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.01 
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    Date
    22. 8.2009 19:51:28
  13. Althoff, K.-D.; Wess, S.; Manago, M.; Bergmann, R.; Maurer, F.; Auriol, E.; Conruyt, N.; Traphöner, R.; Bräuer, M.; Dittrich, S.: Induction and case-based reasoning for classification tasks (1994) 0.01 
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    Source
    Information systems and data analysis: prospects - foundations - applications. Proc. of the 17th Annual Conference of the Gesellschaft für Klassifikation, Kaiserslautern, March 3-5, 1993. Ed.: H.-H. Bock et al
  14. He, W.; Erdelez, S.; Wang, F.-K.; Shyu, C.-R.: ¬The effects of conceptual description and search practice on users' mental models and information seeking in a case-based reasoning retrieval system (2008) 0.01 
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    Abstract
    This paper reportes a study that investigated the effects of conceptual description and search practice on users' mental models and information seeking in a case-based reasoning retrieval (CBR) system with a best match search mechanism. This study also found examined how the presence of a mental model affects the users' search performance and satisfaction in this system. The results of this study revealed that the conceptual description and search practice treatments do not have significantly different effects on the types of user's mental models, search correctness, and search satisfaction. However, the search practice group spent significantly less time than the conceptual description group in finding the results. Qualitative analysis for the subjects' post mental models revealed that subjects in the conceptual description group seem to have more complete mental models of the best match system than those in the search practice group. This study also that subjects with the best match mental models have significantly higher search correctness and search result satisfaction than subjects without the best match mental models. However, the best match mental models do not guarantee less search time in finding the results. This study did not find a significant correlation among search time, search correctness and search satisfaction. The study concludes with suggestions for future research and implications for system developers who are interested in CBR retrieval systems.

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