Intelligent Query Answering by Knowledge Discovery Techniques

Knowledge discovery facilitates querying database knowledge and intelligent query answering in database systems. We investigate the application of discovered knowledge, concept hierarchies, and knowledge discovery tools for intelligent query answering in database systems. A knowledge-rich data model is constructed to incorporate discovered knowledge and knowledge discovery tools. Queries are classified into data queries and knowledge queries. Both types of queries can be answered directly by simple retrieval or intelligently by analyzing the intent of query and providing generalized, neighborhood or associated information using stored or discovered knowledge. Techniques have been developed for intelligent query answering using discovered knowledge and/or knowledge discovery tools, which includes generalization, data summarization, concept clustering, rule discovery, query rewriting, deduction, lazy evaluation, application of multiple-layered databases, etc. Our study shows that knowledge discovery substantially broadens the spectrum of intelligent query answering and may have deep implications on query answering in data- and knowledge-base systems.

[1]  John D. C. Little,et al.  Coverstory: Automated News Finding in Marketing , 1990 .

[2]  Amihai Motro.  Using Integrity Constraints to Provide Intensional Answers to Relational Queries , 1989, VLDB.

[3]  T. J. Teorey,et al.  A logical design methodology for relational databases using the extended entity-relationship model , 1986, CSUR.

[4]  Usama M. Fayyad,et al.  Knowledge Discovery in Databases: An Overview , 1997, ILP.

[5]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .

[6]  David Haussler.  Bias, Version Spaces and Valiant's Learning Framework , 1987 .

[7]  Amihai Motro,et al.  Querying database knowledge , 1990, SIGMOD '90.

[8]  Pat Langley,et al.  An Integrated Approach to Empirical Discovery , 1989 .

[9]  Jiawei Han,et al.  Knowledge Discovery in Databases: An Attribute-Oriented Approach , 1992, VLDB.

[10]  Douglas H. Fisher,et al.  Improving Inference through Conceptual Clustering , 1987, AAAI.

[11]  Jiawei Han,et al.  Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases , 1994, KDD Workshop.

[12]  Ryszard S. Michalski,et al.  A theory and methodology of inductive learning , 1993 .

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

[14]  Jeffrey D. Ullman,et al.  Principles of database and knowledge-base systems, Vol. I , 1988 .

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

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

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

[18]  Tomasz Imeilinski.  Intelligent query answering in rule based systems , 1987 .

[19]  Alain Pirotte,et al.  Constraints for improving the generation of intentional answers in a deductive database , 1989, [1989] Proceedings. Fifth International Conference on Data Engineering.

[20]  Roger King,et al.  Semantic database modeling: survey, applications, and research issues , 1987, CSUR.

[21]  Jeffrey D. Uuman.  Principles of database and knowledge- base systems , 1989 .

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

[23]  Jiawei Han,et al.  Attribute-Oriented Induction in Relational Databases , 1991, Knowledge Discovery in Databases.

[24]  Tomasz Imielinski,et al.  Intelligent Query Answering in Rule Based Systems , 1988, J. Log. Program..

[25]  J TeoreyToby,et al.  A logical design methodology for relational databases using the extended entity-relationship model , 1986 .

[26]  Daniel E. O'Leary,et al.  Knowledge Discovery as a Threat to Database Security , 1991, Knowledge Discovery in Databases.

[27]  Brant A. Cheikes,et al.  Methodological issues in the design of intelligent and cooperative information systems , 1993, [1993] Proceedings International Conference on Intelligent and Cooperative Information Systems.

[28]  J.-W. Han,et al.  Deductive-ER: deductive entity-relationship data model and its data language , 1992, Inf. Softw. Technol..

[29]  Peter P. Chen.  The entity-relationship model: toward a unified view of data , 1975, VLDB '75.

[30]  Laks V. S. Lakshmanan,et al.  On semantic query optimization in deductive databases , 1992, [1992] Eighth International Conference on Data Engineering.

[31]  Frédéric Cuppens,et al.  Cooperative Answering: A Methodology to Provide Intelligent Access to databases , 1988, Expert Database Conf..

[32]  Les Gasser,et al.  Social Conceptions of Knowledge and Action: DAI Foundations and Open Systems Semantics , 1991, Artif. Intell..

[33]  Xiaohua Hu.  Conceptual clustering and concept hierarchies in knowledge discovery , 1992 .

[34]  John Grant,et al.  Logic-based approach to semantic query optimization , 1990, TODS.

[35]  Mieczyslaw M. Kokar.  COPER: a methodology for learning invariant functional descriptions , 1986 .

[36]  Frédéric Cuppens,et al.  How to recognize interesting topics to provide cooperative answering , 1989, Inf. Syst..

[37]  Alexander Borgida,et al.  Loading data into description reasoners , 1993, SIGMOD Conference.

[38]  Jonathan J. King.  QUIST: A System for Semantic Query Optimization in Relational Databases , 1981, VLDB.

[39]  Candace L. Sidner,et al.  Attention, Intentions, and the Structure of Discourse , 1986, CL.

[40]  Amihai Motro.  SEAVE: a mechanism for verifying user presuppositions in query systems , 1986, TOIS.

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

[42]  Timothy W. Finin,et al.  Natural language interactions with artificial experts , 1986, Proceedings of the IEEE.

[43]  Jiawei Han,et al.  Cooperative Query Answering Using Multiple Layered Databases , 1994, CoopIS.

[44]  Richard R. Muntz,et al.  Implicit Representation for Extensional Answers , 1988, Expert Database Conf..

[45]  Larry Kerschberg,et al.  Mining for Knowledge in Databases: Goals and General Description of the INLEN System , 1989, Knowledge Discovery in Databases.

[46]  W. Ziarko,et al.  An application of DATALOGIC/R knowledge discovery tool to identify strong predictive rules in stock market data , 1993 .

[47]  Michael Stonebraker,et al.  Database systems: achievements and opportunities , 1990, SGMD.

[48]  Jeffrey D. Ullman,et al.  Principles Of Database And Knowledge-Base Systems , 1979 .

[49]  Terry Gaasterland,et al.  Restricting query relaxation through user constraints , 1993, [1993] Proceedings International Conference on Intelligent and Cooperative Information Systems.

[50]  Jan M. Zytkow,et al.  Interactive Mining of Regularities in Databases , 1991, Knowledge Discovery in Databases.

[51]  Clement T. Yu,et al.  Automatic Knowledge Acquisition and Maintenance for Semantic Query Optimization , 1989, IEEE Trans. Knowl. Data Eng..

[52]  Jiawei Han,et al.  Exploration of the power of attribute-oriented induction in data mining , 1995, KDD 1995.

[53]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.