Towards Concept-Oriented Databases

Case-based reasoning (CBR) systems define knowledge in terms of a memory or library of past cases and a retrieval mechanism that revolves around retrieving data relevant to a goal query. Additionally, such systems employ an adaptation component that transforms the retrieved data into a solution to the problem expressed by the original query. The combination of goal query and the subsequent solution transformation is referred to as CBR goal query. Goal queries are concerned with data that is close to the request expressed in the query. Conventional relational and object-oriented databases are usually concerned specific queries. Extending conventional object-oriented data models, this paper proposes a concept-oriented data model that provides a variety of mechanisms to support conventional goal and CBR goal queries. It is shown that such a concept-oriented data model could be used as the core for a more general knowledge base management system.

[1]  Werner Dubitzky,et al.  On the automation of case base development from large databases , 1998 .

[2]  D. Hsieh.  A logic to unify semantic network knowledge systems with object-oriented database models , 1992, Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences.

[3]  Jörg Petersen,et al.  Similarity of fuzzy data in a case-based fuzzy system in anaesthesia , 1997, Fuzzy Sets Syst..

[4]  Trevor P Martin,et al.  Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence , 1995 .

[5]  A. Tversky.  Features of Similarity , 1977 .

[6]  David A. Bell,et al.  How Similar is VERY YOUNG to 43 Years of Age? On the Representation and Comparison of Polymorphic Properties , 1997, IJCAI.

[7]  Edward E. Smith,et al.  Categories and concepts , 1984 .

[8]  Shyi-Ming Chen,et al.  Measures of similarity between vague sets , 1995, Fuzzy Sets Syst..

[9]  David A. Bell,et al.  A generic, object‐oriented case‐knowledge representation scheme, and its integration into a wider information management scenario , 1996 .

[10]  C SchankRoger,et al.  Dynamic Memory: A Theory of Reminding and Learning in Computers and People , 1983 .

[11]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[12]  Michael D. Brown.  A Memory Model for Case Retrieval by Activation Passing , 1994 .

[13]  Friedrich Gebhardt,et al.  Survey on structure-based case retrieval , 1997, The Knowledge Engineering Review.

[14]  AlagicSuad.  The ODMG object model , 1997 .

[15]  Agnar Aamodt,et al.  A knowledge-intensive, integrated approach to problem solving and sustained learning , 1992 .

[16]  B. Buckles,et al.  Modelling class hierarchies in the fuzzy object-oriented data model , 1993 .

[17]  Patrick Bosc,et al.  Fuzzy databases : principles and applications , 1996 .

[18]  Tok Wang Ling,et al.  Deductive and Object-Oriented Databases , 1995, Lecture Notes in Computer Science.

[19]  Zhiming Zhang,et al.  Similarity Measures for Retrieval in Case-Based Reasoning Systems , 1998, Appl. Artif. Intell..

[20]  Amihai Motro.  Extending the Relational Database Model to Support Goal Queries , 1986, Expert Database Conf..

[21]  R. G. G. Cattell,et al.  The Object Database Standard: ODMG-93 (Release 1.1) , 1994 .

[22]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[23]  Norbert Fuhr,et al.  A Probabilistic Framework for Vague Queries and Imprecise Information in Databases , 1990, VLDB.

[24]  David A. Bell,et al.  Generalized Union and Project Operations for Pooling Uncertain and Imprecise Information , 1996, Data Knowl. Eng..

[25]  Alan Hutchinson,et al.  Algorithmic Learning , 1994 .

[26]  Werner Dubitzky,et al.  Knowledge integration in case-based reasoning : a concept-centred approach , 1997 .

[27]  Won Kim,et al.  Object-Oriented Concepts, Databases, and Applications , 1989 .

[28]  D. Dubois,et al.  Vagueness, typicality, and uncertainty in class hierarchies , 1991 .

[29]  Ryszard S. Michalski,et al.  Categories and Concepts: Theoretical Views and Inductive Data Analysis , 1993 .

[30]  Ray Bareiss,et al.  Concept Learning and Heuristic Classification in WeakTtheory Domains , 1990, Artif. Intell..

[31]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.