Search (21 results, page 1 of 2)

  • × theme_ss:"Case Based Reasoning"
  1. Ress, D.A.; Young, R.E.: ¬A distributed fuzzy constraint satisfaction system with context-based reasoning (1998) 0.07 
    0.07128575 = product of:
  0.17821437 = sum of:
    0.1129755 = weight(_text_:context in 3842) [ClassicSimilarity], result of:
      0.1129755 = score(doc=3842,freq=8.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.64109284 = fieldWeight in 3842, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3842)
    0.06523886 = weight(_text_:system in 3842) [ClassicSimilarity], result of:
      0.06523886 = score(doc=3842,freq=8.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.4871716 = fieldWeight in 3842, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3842)
  0.4 = coord(2/5)
    Abstract
    Presents a fuzzy constraint satisfaction system which can be used in a distributive environment where large problems can be broken down into smaller constraint networks for easier processing. Identifies contexts which exist within the constraint satisfaction system. Context based reasoning is identified both within and among constraint networks. Outlines the motviation behind the research and describes the fuzzy constraint satisfaction system FuzCon. Points out 3 mappings of the context-based reasoning 'ist' operator to fuzzy constraints and presents an example of designing a printed wiring board
    Footnote
    Contribution to a special issue on using context in computer applications
  2. Ozturk, P.; Aamodt, A.: ¬A context model for knowledge-intensive case-based reasoning (1998) 0.05 
    0.051430937 = product of:
  0.12857734 = sum of:
    0.09129799 = weight(_text_:context in 3844) [ClassicSimilarity], result of:
      0.09129799 = score(doc=3844,freq=4.0), product of:
        0.17622331 = queryWeight, product of:
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.04251826 = queryNorm
        0.51808125 = fieldWeight in 3844, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.14465 = idf(docFreq=1904, maxDocs=44218)
          0.0625 = fieldNorm(doc=3844)
    0.03727935 = weight(_text_:system in 3844) [ClassicSimilarity], result of:
      0.03727935 = score(doc=3844,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.27838376 = fieldWeight in 3844, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0625 = fieldNorm(doc=3844)
  0.4 = coord(2/5)
    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
    Footnote
    Contribution to a special issue on using context in computer applications
  3. Kohno, T.: Error repair and knowledge acquisition via case-based reasoning (1997) 0.03 
    0.03197257 = product of:
  0.07993142 = sum of:
    0.06456973 = weight(_text_:system in 437) [ClassicSimilarity], result of:
      0.06456973 = score(doc=437,freq=6.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.48217484 = fieldWeight in 437, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0625 = fieldNorm(doc=437)
    0.015361699 = product of:
      0.046085097 = sum of:
        0.046085097 = weight(_text_:22 in 437) [ClassicSimilarity], result of:
          0.046085097 = score(doc=437,freq=2.0), product of:
            0.1488917 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04251826 = queryNorm
            0.30952093 = fieldWeight in 437, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=437)
      0.33333334 = coord(1/3)
  0.4 = coord(2/5)
    Abstract
    Proposes a new architecture combining rule-based reasoning (RBR), case based reasoning (CBR) and knowledge acquisition technology in a system which solves pattern search problems. Details the pattern search problem, the system architecture and functions, error repair method via case-based reasoning, the knowledge acquisition method, system evaluation, and discusses related work
    Date
    6. 3.1997 16:22:15
  4. Golding, A.R.; Rosenbloom, P.S.: Improving accuracy by combining rule-based and case-based reasoning (1996) 0.03 
    0.027233064 = product of:
  0.06808266 = sum of:
    0.05272096 = weight(_text_:system in 607) [ClassicSimilarity], result of:
      0.05272096 = score(doc=607,freq=4.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.3936941 = fieldWeight in 607, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0625 = fieldNorm(doc=607)
    0.015361699 = product of:
      0.046085097 = sum of:
        0.046085097 = weight(_text_:22 in 607) [ClassicSimilarity], result of:
          0.046085097 = score(doc=607,freq=2.0), product of:
            0.1488917 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04251826 = queryNorm
            0.30952093 = fieldWeight in 607, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=607)
      0.33333334 = coord(1/3)
  0.4 = coord(2/5)
    Abstract
    Presents an architeture for combining rule-based and case-based reasoning. It is applied to the problem of name pronunciation. Presents the system independent of the domain of name pronunciation. Describes the Anapron system, which instantiates the architecture for name pronunciation. Presents a set of experiments on Anapron, the key result being an empirical demonstration of the improvement obtained by combining rules and cases. Discusses related work
    Date
    6. 3.1997 16:22:15
  5. Ram, A.; Santamaria, J.C.: Continuous case-based reasoning (1997) 0.03 
    0.027233064 = product of:
  0.06808266 = sum of:
    0.05272096 = weight(_text_:system in 435) [ClassicSimilarity], result of:
      0.05272096 = score(doc=435,freq=4.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.3936941 = fieldWeight in 435, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0625 = fieldNorm(doc=435)
    0.015361699 = product of:
      0.046085097 = sum of:
        0.046085097 = weight(_text_:22 in 435) [ClassicSimilarity], result of:
          0.046085097 = score(doc=435,freq=2.0), product of:
            0.1488917 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04251826 = queryNorm
            0.30952093 = fieldWeight in 435, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=435)
      0.33333334 = coord(1/3)
  0.4 = coord(2/5)
    Abstract
    Introduces a new method for continuous case-based reasoning, and discusses its applications to the dynamic selection, modification and acquisition of robot bahaviours in an autonomous navigation system, SINS (self-improving navigation system): The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. Discusses case-based reasoning issues addressed by this research
    Date
    6. 3.1997 16:22:15
  6. 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) 0.02 
    0.02397943 = product of:
  0.05994857 = sum of:
    0.048427295 = weight(_text_:system in 1449) [ClassicSimilarity], result of:
      0.048427295 = score(doc=1449,freq=6.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.36163113 = fieldWeight in 1449, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.046875 = fieldNorm(doc=1449)
    0.011521274 = product of:
      0.03456382 = sum of:
        0.03456382 = weight(_text_:22 in 1449) [ClassicSimilarity], result of:
          0.03456382 = score(doc=1449,freq=2.0), product of:
            0.1488917 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04251826 = queryNorm
            0.23214069 = fieldWeight in 1449, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=1449)
      0.33333334 = coord(1/3)
  0.4 = coord(2/5)
    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
  7. Mahapatra, R.; Sen, A.: Case base management systems : providing database support to case-based reasoners (1994) 0.02 
    0.021112198 = product of:
  0.052780494 = sum of:
    0.03727935 = weight(_text_:system in 525) [ClassicSimilarity], result of:
      0.03727935 = score(doc=525,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.27838376 = fieldWeight in 525, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0625 = fieldNorm(doc=525)
    0.015501143 = product of:
      0.04650343 = sum of:
        0.04650343 = weight(_text_:29 in 525) [ClassicSimilarity], result of:
          0.04650343 = score(doc=525,freq=2.0), product of:
            0.14956595 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.04251826 = queryNorm
            0.31092256 = fieldWeight in 525, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0625 = fieldNorm(doc=525)
      0.33333334 = coord(1/3)
  0.4 = coord(2/5)
    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
    Source
    Journal of database management. 5(1994) no.2, S.19-29
  8. Kolodner, J.: Case-based reasoning (1993) 0.01 
    0.014352133 = product of:
  0.07176066 = sum of:
    0.07176066 = weight(_text_:index in 526) [ClassicSimilarity], result of:
      0.07176066 = score(doc=526,freq=2.0), product of:
        0.18579477 = queryWeight, product of:
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.04251826 = queryNorm
        0.3862362 = fieldWeight in 526, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.369764 = idf(docFreq=1520, maxDocs=44218)
          0.0625 = fieldNorm(doc=526)
  0.2 = coord(1/5)
    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
  9. Mazzucchelli, A.; Sartori , F.: String similarity in CBR platforms : a preliminary study (2014) 0.01 
    0.013049269 = product of:
  0.06524634 = sum of:
    0.06524634 = product of:
      0.097869515 = sum of:
        0.05754506 = weight(_text_:29 in 1568) [ClassicSimilarity], result of:
          0.05754506 = score(doc=1568,freq=4.0), product of:
            0.14956595 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.04251826 = queryNorm
            0.38474706 = fieldWeight in 1568, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1568)
        0.04032446 = weight(_text_:22 in 1568) [ClassicSimilarity], result of:
          0.04032446 = score(doc=1568,freq=2.0), product of:
            0.1488917 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04251826 = queryNorm
            0.2708308 = fieldWeight in 1568, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1568)
      0.6666667 = coord(2/3)
  0.2 = coord(1/5)
    Pages
    S.22-29
    Source
    Metadata and semantics research: 8th Research Conference, MTSR 2014, Karlsruhe, Germany, November 27-29, 2014, Proceedings. Eds.: S. Closs et al
  10. 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 
    0.010419896 = product of:
  0.052099477 = sum of:
    0.052099477 = weight(_text_:system in 2036) [ClassicSimilarity], result of:
      0.052099477 = score(doc=2036,freq=10.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.38905317 = fieldWeight in 2036, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2036)
  0.2 = coord(1/5)
    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.
  11. He, W.; Tian, X.: ¬A longitudinal study of user queries and browsing requests in a case-based reasoning retrieval system (2017) 0.01 
    0.008071217 = product of:
  0.04035608 = sum of:
    0.04035608 = weight(_text_:system in 3600) [ClassicSimilarity], result of:
      0.04035608 = score(doc=3600,freq=6.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.30135927 = fieldWeight in 3600, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3600)
  0.2 = coord(1/5)
    Abstract
    This article reports on a longitudinal analysis of query logs of a web-based case library system during an 8-year period (from 2005 to 2012). The analysis studies 3 different information-seeking approaches: keyword searching, browsing, and case-based reasoning (CBR) searching provided by the system by examining the query logs that stretch over 8 years. The longitudinal dimension of this study offers unique possibilities to see how users used the 3 different approaches over time. Various user information-seeking patterns and trends are identified through the query usage pattern analysis and session analysis. The study identified different user groups and found that a majority of the users tend to stick to their favorite information-seeking approach to meet their immediate information needs and do not seem to care whether alternative search options will offer greater benefits. The study also found that return users used CBR searching much more frequently than 1-time users and tend to use more query terms to look for information than 1-time users.
  12. 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 
    0.006523886 = product of:
  0.03261943 = sum of:
    0.03261943 = weight(_text_:system in 7744) [ClassicSimilarity], result of:
      0.03261943 = score(doc=7744,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.2435858 = fieldWeight in 7744, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0546875 = fieldNorm(doc=7744)
  0.2 = coord(1/5)
    Abstract
    We present 2 techniques for reasoning from cases to solve classification tasks: induction and case-based reasoning. We contrast the 2 technologies (that are often confused) and show how they complement each other. Based on this, we describe how they are integrated in one single platform for reasoning from cases: the INRECA system
  13. Löckenhoff, H.: Case-Based Teaching/Learning for issue orientation and control (1996) 0.01 
    0.006523886 = product of:
  0.03261943 = sum of:
    0.03261943 = weight(_text_:system in 5205) [ClassicSimilarity], result of:
      0.03261943 = score(doc=5205,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.2435858 = fieldWeight in 5205, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5205)
  0.2 = coord(1/5)
    Abstract
    Case Based Reasoning (CBR) is discussed in connection with a wide variety of knowledge aspects. Obviously knowledge acquisition under conditions of rapid and increasingly disruptive change will necessarily rely on the proper use of case experience. The following remarks emerged from practice oriented teaching/learning in the domians of social sciences, practical philosophy, didactics and epistemology. The main interest will be directed to methodical concepts of knowledge transfer, in particular to didactics and learning within the teaching/learning system
  14. Dearden, A.M.; Harrison, M.D.: Abstract models for HCI (1997) 0.01 
    0.006523886 = product of:
  0.03261943 = sum of:
    0.03261943 = weight(_text_:system in 794) [ClassicSimilarity], result of:
      0.03261943 = score(doc=794,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.2435858 = fieldWeight in 794, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0546875 = fieldNorm(doc=794)
  0.2 = coord(1/5)
    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
  15. Jaenecke, P.: Erkenntnistheoretische Untersuchungen über fallbezogenes Schlußfolgern (1996) 0.01 
    0.0055919024 = product of:
  0.027959513 = sum of:
    0.027959513 = weight(_text_:system in 5204) [ClassicSimilarity], result of:
      0.027959513 = score(doc=5204,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.20878783 = fieldWeight in 5204, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.046875 = fieldNorm(doc=5204)
  0.2 = coord(1/5)
    Abstract
    Mangelndes methodisches Bewußtsein in der Künstlichen Intelligenz hat zur Folge, daß Methoden, die auf einer einzigen zunächst vielversprechenden Idee beruhen, zugunsten anderer fallengelassen werden, sowie sich ernste Schwierigkeiten ergeben. Doch für Methoden gilt nicht das Alles-oder-Nichts-Prinzip; Ziel muß es daher sein, verschiedene sich hinsichtlich ihrer Anwendbarkeit einander ergänzende Methoden aufeinander abzustimmen und zu einem arbeitsfähigen System zu vereinen; das trifft insbesondere auf das bereits vom Ansatz her auf Methodenvielfalt angelegte fallbezogene Schlußfolgern zu. Die von den Kognitionswissenschaften gebotenen Voraussetzungen sind jedoch für solch ein Vorhaben nicht günstig. Es herrscht ein Theorien- und Modellwirrwar, das zu einem Wirrwar von Begriffen, Sichtweisen und Verfahren geführt hat. Die vorliegende Arbeit skizziert einen Ausweg aus dieser unbefriedigenden Situation. Sie orientiert sich an den Fragen 'welche Aufgaben sollen mit fallbezogenem Schlußfolgern gelöst werden?', 'durch welche Merkmale ist dieser Ansatz charakterisiert?' und beschäftigt sich abschließend mit den sich aus den Antworten ergebenden Folgerungen
  16. Rissland, E.L.; Daniels, J.J.: ¬The synergistic application of CBR to IR (1996) 0.01 
    0.0055919024 = product of:
  0.027959513 = sum of:
    0.027959513 = weight(_text_:system in 7736) [ClassicSimilarity], result of:
      0.027959513 = score(doc=7736,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.20878783 = fieldWeight in 7736, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.046875 = fieldNorm(doc=7736)
  0.2 = coord(1/5)
    Abstract
    Discusses a hybrid approach combining case-based reasoning and information retrieval for the retrieval of full text documents. It takes as input a standard symbolic representation of a problem case and retrieves text of relevant cases from a document collection dramatically larger than the case base available to the CBR system. It works by performing a standard HYPO style analysis and uses the texts associated with important classes of cases found in this analysis to seed a modified version of INQUERY's relevance feedback mechanism in order to generate a query composed of individual terms or pairs of terms. It exteds the reach of CBR to much larger corpora, and it anbales the injection of knowledge based techniques into traditional IR. Describes the CBR-IR approach and reports on-going experiments
  17. Analogie in der Wissensrepräsentation: Case-Based Reasoning und räumliche Modelle : 4. Tagung der deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Trier, 17.-20. Oktober 1995 (1996) 0.00 
    0.0046599186 = product of:
  0.023299592 = sum of:
    0.023299592 = weight(_text_:system in 5201) [ClassicSimilarity], result of:
      0.023299592 = score(doc=5201,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.17398985 = fieldWeight in 5201, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5201)
  0.2 = coord(1/5)
    Abstract
    Enthält die Beiträge: CZAP, H.: Einführung in die Wissensorganisation und Case-Based Reasoning (CBR); ALTHOFF, K.-D., R. TRAPHÖNER u. S. WESS: Efficient integration on induction and Case-Base Reasoning: the INRECA System; SCHIEMANN, I. u. A. WOLTERING: Fallspeicherorganisation in der CBR-Shell Janus; COULON, C.-H.: Die Rolle des Anpassungswissens im CBR (Am Beispiel der Ausnutzung von Struktur im CBR); SCHAAF, J.W.: Fischen und Versenken: ein anytime-Algorithmus zur Suche nach situationsgerechten Fällen; JAENECKE, P.: Erkenntnistheoretische Untersuchungen über fallbezogenes Schlußfolgern; LÖCKENHOFF, H.: Cabse-Based Teaching/Learning for issue orientation and control; BIES, W.: 'Denken in Bildern': zu den Metaphern der Wissensorganisation; PRIBBENOW, S.: Räumliches Wissen: zur Interaktion von Logik und Bildern; STOLLE, M. u. V. KIRCHBERG: Mental maps in der Stadtforschung: Grundlage und Perspektiven; BAYER, H. u. R. BAUEREISS: Der Familienatlas als sozialräumliche Information; HARDT, F., G. TASSOUKIS u. H.P. OHLY: Räumliche Hintergrundinformation in bibliographischen Datenbanken; SALENTIN, K.: Geodemographische Ansätze beim Sampling im Direktmarketingverfahren; PIERAU, K., G. NARWELEIT u. H. THÜMMLER: Entwicklung eines Geographisch-Historischen Informationssystems; LENSKI, W. u. E. WETTE-ROCH: Terminologie und Wissensrepräsentation in pragmatischer Sichtweise; FUGMANN, R.: Die Entlineaririserung und Strukturierung von Texten zur Inhaltserschließung und Wissensrepräsentation; LORENZ, B.: Überlegungen zur Verbundklassifikation; NACKE, O.: Ein einfaches Verfahren zur Analyse großer Wissensmengen; BOL, G., E. HOTZ u. T. STÜTZLE: Neuronale Netze zur Klassifikation von Fehlrern in der statistischen Prozeßregulierung
  18. Sauer, C.S.: Analyse von Webcommunities und Extraktion von Wissen aus Communitydaten für Case-Based Reasoning Systeme (2010) 0.00 
    0.0046599186 = product of:
  0.023299592 = sum of:
    0.023299592 = weight(_text_:system in 2231) [ClassicSimilarity], result of:
      0.023299592 = score(doc=2231,freq=2.0), product of:
        0.13391352 = queryWeight, product of:
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.04251826 = queryNorm
        0.17398985 = fieldWeight in 2231, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1495528 = idf(docFreq=5152, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2231)
  0.2 = coord(1/5)
    Abstract
    Die vorliegende Arbeit befasst sich mit den Möglichkeiten der Informationsextraktion aus den Daten von Webcommunities und der Verwendung der extrahierten Informationen in Case-Based Reasoning- (CBR) Systemen. Im Rahmen der Arbeit wird auf die Entwicklung der Webcommunities im Zeitraum der letzten 30 Jahre eingegangen. Es wird eine Klassifikation der derzeitig anzutreffenden Webcommunities in verschiedene Kategorien von Webcommunities vorgenommen. Diese Klassifikation erfolgt hinsichtlich der Struktur, der technischen Mittel sowie der Interessen der Nutzer dieser Webcommunities. Aufbauend auf die vorgenommene Klassifikation von Webcommunities erfolgt eine Untersuchung der Eignung dieser Kategorien von Webcommunities zur Informationsextraktion im Kontext der Verwendung der extrahierten Informationen in CBR-Systemen. Im selben Kontext werden verschiedene Ansätze und Techniken der Informationsextraktion auf ihre Eignung zur Extraktion von Wissen speziell für die Wissenscontainer von CBR -Systeme geprüft. Aufbauend auf den dadurch gewonnenen Erkenntnissen wird, angelehnt an den Prozess der Knowledge Discovery in Databases, ein eigenes Prozessmodell der Wissensextraktion aus Webcommunities für CBR-Systeme entworfen. Im Zuge der näheren Betrachtung dieses Prozessmodells wird auf verschiedene, durch die beabsichtigte Verwendung der extrahierten Informationen in den vier Wissenscontainern des CBR bedingte, Anforderungen an NLP- sowie IE-Techniken, die zur Extraktion dieser Daten verwendet werden, eingegangen. Die in den theoretischen Betrachtungen erlangten Erkenntnisse werden dann dazu eingesetzt, eine Anwendung zur Informationsextraktion aus einer Webcommunity für ein CBR-System, in Form der Knowledge Extraction Workbench zu implementieren. Diese IEAnwendung arbeitet im Kontext des auf der SEASALT-Architektur aufbauenden Projektes docQuery. Die Realisierung dieser IE-Anwendung wird dokumentiert sowie die Extraktionsergebnisse der Anwendung hinsichtlich ihres Umfanges und ihrer Qualität evaluiert.
  19. Chen, Z.: ¬A conceptual model for storage and retrieval of short scientific texts (1993) 0.00 
    0.003875286 = product of:
  0.01937643 = sum of:
    0.01937643 = product of:
      0.05812929 = sum of:
        0.05812929 = weight(_text_:29 in 2715) [ClassicSimilarity], result of:
          0.05812929 = score(doc=2715,freq=2.0), product of:
            0.14956595 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.04251826 = queryNorm
            0.38865322 = fieldWeight in 2715, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.078125 = fieldNorm(doc=2715)
      0.33333334 = coord(1/3)
  0.2 = coord(1/5)
    Source
    Information processing and management. 29(1993) no.2, S.209-214
  20. Mataras, R.L.D.; Plaza, E.: Case-based reasoning : an overview (1997) 0.00 
    0.003875286 = product of:
  0.01937643 = sum of:
    0.01937643 = product of:
      0.05812929 = sum of:
        0.05812929 = weight(_text_:29 in 436) [ClassicSimilarity], result of:
          0.05812929 = score(doc=436,freq=2.0), product of:
            0.14956595 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.04251826 = queryNorm
            0.38865322 = fieldWeight in 436, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.078125 = fieldNorm(doc=436)
      0.33333334 = coord(1/3)
  0.2 = coord(1/5)
    Source
    AI communications. 10(1997) no.1, S.21-29

Authors

Years

Languages

Types

Themes