Automatic Knowledge Discovery in Larger Scale Knowledge—Data Bases

Publisher Summary This chapter presents a methodology for increasing both autonomy and versatility of a discovery system, and the framework of the global learning scheme (GLS) system based on this methodology. It describes the background and goal of the GLS system. This chapter describes two knowledge-discovery-in-databases (KDD) agents in the GLS system, which are, knowledge-oriented statistical inference (KOSI) for discovering structural characteristics from databases, and inheritance-inference-based refinement (IIBR) for generating, managing, and refining the discovered structural characteristics. This chapter explains how to plan and organize the discovery process and how to manage the KDD agents. GLS is mostly similar to INLEN. In INLEN, a database, a knowledge base, and several existing methods of machine learning are integrated as several operators. These operators can generate different kinds of knowledge about the properties and regularities existing in the data. INLEN was implemented as a toolkit like GLS. GLS can dynamically plan and organize the discovery process performed in a distributed cooperative mode for different discovery tasks. Refinement of knowledge is one of the important capabilities of GLS that was not developed in INLEN. This chapter closes with an account on future developments.

[1]  Ning Zhong,et al.  System for managing and refining structural characteristics discovered from databases , 1996, Knowl. Based Syst..

[2]  G. K. Bhattacharyya,et al.  Statistical Concepts And Methods , 1978 .

[3]  N. Zhong,et al.  Data Mining: A Probabilistic Rough Set Approach , 1998 .

[4]  Willi Klösgen,et al.  A Support System for Interpreting Statistical Data , 1991, Knowledge Discovery in Databases.

[5]  Jan M. Zytkow,et al.  Data-Driven Approaches to Empirical Discovery , 1989, Artif. Intell..

[6]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[7]  M. Minsky.  The Society of Mind , 1986 .

[8]  Ning Zhong,et al.  A Way of Increasing Both Autonomy and Versality of a KDD System , 1997, ISMIS.

[9]  Yoshitsugu Kakemoto,et al.  KDD Process Planning , 1997, KDD.

[10]  Herbert A. Simon,et al.  Scientific discovery: compulalional explorations of the creative process , 1987 .

[11]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[12]  Thomas Ellman,et al.  Explanation-based learning: a survey of programs and perspectives , 1989, CSUR.

[13]  Yoshitsugu Kakemoto,et al.  An Organized Society of Autonomous Knowledge Discovery Agents , 1997, CIA.

[14]  Ning Zhong,et al.  A Hierarchical Model Learning Approach for Refining and Managing Concept Clusters Discovered from Databases , 1996, Data Knowl. Eng..

[15]  R. M. Alexander.  How Dinosaurs Ran , 1991 .

[16]  H. Akaike.  A new look at the statistical model identification , 1974 .

[17]  Setsuo Ohsuga,et al.  A way of designing knowledge based systems , 1995, Knowl. Based Syst..

[18]  Neil C. Rowe.  Inheritance of Statistical Properties , 1982, AAAI.

[19]  Tom M. Mitchell,et al.  Generalization as Search , 2002 .

[20]  Ning Zhong,et al.  Discovering Concept Clusters by Decomposing Databases , 1994, Data Knowl. Eng..

[21]  Reidar Conradi,et al.  Automatic Replanning of Task Networks for Process Model Evolution in EPOS , 1993, ESEC.

[22]  Neil C. Rowe,et al.  Management of Regression-Model Data , 1991, Data Knowl. Eng..

[23]  Pat Langley,et al.  Models of Incremental Concept Formation , 1990, Artif. Intell..

[24]  Ning Zhong,et al.  Toward a Multi-Strategy and Cooperative Discovery System , 1995, KDD.

[25]  Jan M. Zytkow,et al.  Introduction: Cognitive autonomy in machine discovery , 1993, Machine Learning.

[26]  Willi Klösgen,et al.  Problems for knowledge discovery in databases and their treatment in the statistics interpreter explora , 1992, Int. J. Intell. Syst..

[27]  Pat Langley,et al.  Elements of Machine Learning , 1995 .

[28]  Christer Bäckström,et al.  Incremental planning , 1996 .

[29]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[30]  Ning Zhong,et al.  The GLS Discovery System: Its Goal, Architecture and Current Results , 1994, ISMIS.

[31]  Douglas H. Fisher,et al.  Conceptual Clustering, Learning from Examples, and Inference , 1987 .

[32]  Chunnian Liu.  Software Process Planning and Execution: Coupling vs. Integration , 1991, CAiSE.

[33]  Larry Kerschberg,et al.  Mining for knowledge in databases: The INLEN architecture, initial implementation and first results , 2004, Journal of Intelligent Information Systems.

[34]  Jadzia Cendrowska,et al.  PRISM: An Algorithm for Inducing Modular Rules , 1987, Int. J. Man Mach. Stud..

[35]  Michael Lebowitz.  Categorizing numeric information for generalization , 1985 .

[36]  Gregory Piatetsky-Shapiro,et al.  Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.

[37]  Setsuo Ohsuga.  Framework of knowledge-based systems : Multiple meta-level architecture for representing problems and problem-solving processes , 1990, Knowl. Based Syst..

[38]  James Kelly,et al.  AutoClass: A Bayesian Classification System , 1993, ML.

[39]  Jiawei Han,et al.  Data-Driven Discovery of Quantitative Rules in Relational Databases , 1993, IEEE Trans. Knowl. Data Eng..

[40]  Philip K. Chan,et al.  Systems for Knowledge Discovery in Databases , 1993, IEEE Trans. Knowl. Data Eng..

[41]  Gregory Piatetsky-Shapiro,et al.  Knowledge discovery workbench for exploring business databases , 1992, Int. J. Intell. Syst..

[42]  Wojciech Ziarko,et al.  The Discovery, Analysis, and Representation of Data Dependencies in Databases , 1991, Knowledge Discovery in Databases.

[43]  Kenneth D. Forbus.  Qualitative Process Theory , 1984, Artif. Intell..

[44]  C RoweNeil.  Antisampling for Estimation , 1985 .

[45]  Robert Engels,et al.  Planning Tasks for Knowledge Discovery in Databases; Performing Task-Oriented User-Guidance , 1996, KDD.