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  1. Kohno, T.: Error repair and knowledge acquisition via case-based reasoning (1997) 0.34 
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    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
    Theme
    Case Based Reasoning
  2. Ram, A.; Santamaria, J.C.: Continuous case-based reasoning (1997) 0.31 
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    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
    Theme
    Case Based Reasoning
  3. Golding, A.R.; Rosenbloom, P.S.: Improving accuracy by combining rule-based and case-based reasoning (1996) 0.29 
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    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
    Theme
    Case Based Reasoning
  4. #2935 0.24 
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    Object
    CBR -> Sachgruppe: Case Based Reasoning
  5. Veleso, M.; Munoz-Avila, H.; Bergmann, R.: Case-based planning : selected methods and systems (1996) 0.21 
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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
    Theme
    Case Based Reasoning
  6. Mazzucchelli, A.; Sartori , F.: String similarity in CBR platforms : a preliminary study (2014) 0.20 
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    Abstract
    Case Based Reasoning is a very important research trend in Artificial Intelligence and can be a powerful approach in the solution of complex problems characterized by heterogeneous knowledge. In this paper we present an ongoing research project where CBR is exploited to support the identification of enterprises potentially going to bankruptcy, through a comparison of their balance indexes with the ones of similar and already closed firms. In particular, the paper focuses on how developing similarity measures for strings can be profitably supported by metadata models of case structures and semantic methods like Query Expansion.
    Pages
    S.22-29
    Theme
    Case Based Reasoning
  7. 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) 0.18 
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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
    Theme
    Case Based Reasoning
  8. Aamodt, A.; Plaza, E.: Case-based reasoning : foundation issues, methodological variations, and systems approaches (1994) 0.18 
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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
    Theme
    Case Based Reasoning
  9. Chen, Z.: ¬A conceptual model for storage and retrieval of short scientific texts (1993) 0.18 
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    Abstract
    A conceptual model for integrating short scientific texts is described, which extends classical text storage and retrieval. A brief comparison with related approaches (such as case-based reasoning and classification-based reasoning) is also provided
    Theme
    Case Based Reasoning
  10. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.18 
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    Abstract
    Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.
    Date
    16.11.2018 14:22:01
  11. Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023) 0.18 
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    Abstract
    Logical rules are essential for uncovering the logical connections between relations, which could improve the reasoning performance and provide interpretable results on knowledge graphs (KGs). Although there have been many efforts to mine meaningful logical rules over KGs, existing methods suffer from the computationally intensive searches over the rule space and a lack of scalability for large-scale KGs. Besides, they often ignore the semantics of relations which is crucial for uncovering logical connections. Recently, large language models (LLMs) have shown impressive performance in the field of natural language processing and various applications, owing to their emergent ability and generalizability. In this paper, we propose a novel framework, ChatRule, unleashing the power of large language models for mining logical rules over knowledge graphs. Specifically, the framework is initiated with an LLM-based rule generator, leveraging both the semantic and structural information of KGs to prompt LLMs to generate logical rules. To refine the generated rules, a rule ranking module estimates the rule quality by incorporating facts from existing KGs. Last, a rule validator harnesses the reasoning ability of LLMs to validate the logical correctness of ranked rules through chain-of-thought reasoning. ChatRule is evaluated on four large-scale KGs, w.r.t. different rule quality metrics and downstream tasks, showing the effectiveness and scalability of our method.
    Date
    23.11.2023 19:07:22
  12. Issues and applications of case-based reasoning in design (1997) 0.17 
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    Theme
    Case Based Reasoning
  13. Kolodner, J.: Case-based reasoning (1993) 0.17 
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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
    Theme
    Case Based Reasoning
  14. 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) 0.15 
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    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
    Theme
    Case Based Reasoning
  15. Lee, E.S.; Zhu, Q.: Fuzzy and evidece reasoning (1995) 0.15 
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    Abstract
    This volume gives readers a complete picture of fuzzy approximate reasoning and uncertainty reasoning based on evidence theory. The topics are systematically treated from fundamentals to advanced issues. Numerous examples illustrate the concepts
  16. Lakemeyer, G.: Relevance from an epistemic perspective (1997) 0.15 
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    Abstract
    Studies relevance relations in the context of propositional logical theories and subject matters or topics of interest, which are taken to be sets of atomic propositions. Introduces the class of regular belief models and a notion of prime implicates applicable to all forms of this type of belief. Introduces implicit belief, which has a classical possible world model. discusses definitions of relevance for regular belief using implicit belief as a running example. Focuses on connections between relevance under implicit belief and work in the literature and computational aspects. Discusses relevance under explicit belief, which is based on a 3 valued extension of possible world semantics
    Date
    6. 3.1997 16:22:15
    Footnote
    Contribution to a special issue devoted to relevance in learning and reasoning systems
  17. Tsatsaoulis, C.; Cheng, Q.; Wei, H.-Y.: Integrating case-based reasoning and decision theory (1997) 0.15 
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    Abstract
    Reports on a methodology that lets case-based reasoning use decision-theoretic approaches to deal with unknown proble features and how to make decisions in the presence of these unknowns. Implements the methodology in a case-based design assistant that helps chemists design pharmaceuticals
    Theme
    Case Based Reasoning
  18. Mataras, R.L.D.; Plaza, E.: Case-based reasoning : an overview (1997) 0.14 
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    Theme
    Case Based Reasoning
  19. Ress, D.A.; Young, R.E.: ¬A distributed fuzzy constraint satisfaction system with context-based reasoning (1998) 0.14 
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    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
    Theme
    Case Based Reasoning
  20. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.14 
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    Abstract
    Document representations for text classification are typically based on the classical Bag-Of-Words paradigm. This approach comes with deficiencies that motivate the integration of features on a higher semantic level than single words. In this paper we propose an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting is used for actual classification. Experimental evaluations on two well known text corpora support our approach through consistent improvement of the results.
    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32

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