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Using domain knowledge in knowledge discovery

Authors:

Suk-Chung Yoon,

Lawrence J. Henschen,

Sam MakkiAuthors Info & Claims

CIKM '99: Proceedings of the eighth international conference on Information and knowledge management

Pages 243 - 250

https://doi.org/10.1145/319950.320008

Published: 01 November 1999 Publication History

Abstract

With the explosive growth of the size of databases, many knowledge discovery applications deal with large quantities of data. There is an urgent need to develop methodologies which will allow the applications to focus search to a potentially interesting and relevant portion of the data, which can reduce the computational complexity of the knowledge discovery process and improve the interestingness of discovered knowledge. Previous work on semantic query optimization, which is an approach to take advantage of domain knowledge for query optimization, has demonstrated that significant cost reduction can be achieved by reformulating a query into a less expensive yet equivalent query which produces the same answer as the original one. In this paper, we introduce a method to utilize three types of domain knowledge in reducing the cost of finding a potentially interesting and relevant portion of the data while improving the quality of discovered knowledge. In addition, we propose a method to select relevant domain knowledge without an exhaustive search of all domain knowledge. The contribution of this paper is that we lay out a general framework for using domain knowledge in the knowledge discovery process effectively by providing guidelines.

References

[1]

R. Agrawal, et.al., "Mining Association Rules between Sets of Items in Large Databases", Proceedings of A CM $IGMOD, pp.207-216, 1993

Digital Library

[2]

R. Agrawal and R. Srikant, "Mining Sequential Patterns", Proceedings of the Eleventh International Conference on Data Engineering, pp.3-14, 1995

Digital Library

[3]

F. Bancilhon, et.al., Building an Object-Oriented Database System, Morgan Kaufmann Publishers, I992

Digital Library

[4]

U.S. Charkravarthy, et.al., "Logic-based Approach to Semantic Query Optimization", ACM Transaction on Database Systems, 15(2): pp.162- 207, 1990

Digital Library

[5]

V. Dhar and A. Tuzhilin, "Abstract-Driven Pattern Discovery in Databases", IEEE Transactions on I(nowledge and Data Engineering, vol. 5, no. 6, pp. 926-938, 1993

Digital Library

[6]

W.J. Frawley, et.al., "Knowledge Discovery in Databases: An Overview", Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W.J. Frawley(Eds), pp. 1-27, AAAI/MIT Press, t991

[7]

J. Freytag, et.al., Query Processing For Advanced Database Systems, Morgan Kaufmann Publishers, 1994

Digital Library

[8]

H. Gallaire, et.al., "Logic and Database: A Deductive Approach", Computing Surveys, vol. 16, no. 1, pp. 154-185, 1984

Digital Library

[9]

J. Hen, et.al., "Data-Driven Discovery of Quantitative Rules in Relational Datbases", IEEE Transactions on Knowledge and Data Engineering, vol. 5, no. 1, pp.29-40, 1993

Digital Library

[10]

M. Jarke, "Semantic Query Optimizat~on in Expert Systems and Database Systems", Proceedings of the First International Conference on Expert Database Systems, pp. 467-482, 1984

Digital Library

[11]

K.A. Kaufman, et.al., "Mining for Knowledge in Databases: Goals and General Description of the INLEN System", Knowledge Discovery in Databases, G. Piatetsky and W. Frawley(Eds.), pp.449-462, AAAi/MIT Press, 1991

[12]

W. Kim, Introduction to Object-Oriented Databases, MIT Press, 1990

Digital Library

[13]

M. Klemettinen, et.al., "Finding Interesting Rules from Large Sets of Discovered Association Rules", Proceedings of the Third A CM International Conference on Information and Knowledge Management, pp. 401-408, 1994

Digital Library

[14]

J. W. Lloyd, Foundation of Logic Programming, Springer-Verlag, 1984

Digital Library

[15]

J.S. Park, et.al., "An Effective Hash-Based Algorithm for Mining Association Rules", Proceedings of ACM SIGMOD, pp.175-186, 1995

Digital Library

[16]

G. Piatetsky-Shapiro, and W.J. Frawley, Eds., Knowledge Discovery in Databases, AAAI/MIT Press, 1991

Digital Library

[17]

G. Piatetsky-Shapiro, "Discovery, Analysis and Presentation of Strong Rules", ffnowledge Discovery in Databases, G. Piatetsky and W. Frawley(Eds.), pp.229-248, AAAI/MIT Press, 1991

[18]

E. Rundensteiner, "A Classification Algorithm for Supporting Object-orienetd Views", Proceedings of the Third ACM International Conference on Information and Knowledge Management, pp. 18-25, 1994

Digital Library

[19]

W. Shen, et.al., "Metaqueries for Data Mining'', .Advanced in Kno~vledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Eds.), pp. 375-398, AAAI/MIT Press, 1996

Digital Library

[20]

A. Silberschatz, et.al., "Database Systems: Achievement and Opportunities", Communication of AUM, 34:94-109, 1991

Digital Library

[21]

M. D. Siegel, "Automatic Rule Derivation for Semantic Query Optimization", Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W. Frawley(Eds.), pp.411-427, AAAI/MIT Press, 1991

[22]

J. Ullman, Principles of Database and Knowledge-base Systems, Vol I and II, Computer Science Press, 1988

Digital Library

[23]

S. C. Yoon, et.al., "Intelligent Query Answering in Deductive and Object-Oriented Databases", Proceedings of the Third A CM International Conferenece on Information and Kno~vledge Management, pp.244-251, 1994

Digital Library

[24]

S. C. Yoon, et.al.,"Semantic Query Processing in Deductive Object-Oriented Databases", Proceedings of the Fourth A CM International Conference on Information and Kno~vledge Management, pp.150-157, 1995

Digital Library

[25]

D. Xu, "Search Control in Semantic Query Optimization'', University of Massachusetts, Department of Computer Science, Tech Report TR83-09, 1983

[26]

S. Zdonik, and D. Mater, Readings in Object- Oriented Database Systems, Morgan Kaufmann Publisher, 1990.

Digital Library

Cited By

Esnaola-Gonzalez IBermúdez JFernandez IArnaiz A(2021)EEPSA as a core ontology for energy efficiency and thermal comfort in buildingsApplied Ontology10.3233/AO-21024516:2(193-228)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/AO-210245
Charfi MGripay YPetit JDesai BToyama MBernardino J(2016)Optimization of a Class of Temporal QueriesProceedings of the 20th International Database Engineering & Applications Symposium10.1145/2938503.2938514(346-351)Online publication date: 11-Jul-2016
https://dl.acm.org/doi/10.1145/2938503.2938514
Yahya BSong MBae HSul SWu J(2016)Domain-driven actionable process model discoveryComputers and Industrial Engineering10.1016/j.cie.2016.05.01099:C(382-400)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.cie.2016.05.010
Show More Cited By

Index Terms

Using domain knowledge in knowledge discovery
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing
  2. Information systems applications
    1. Data mining
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

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Published In

cover image ACM Conferences

CIKM '99: Proceedings of the eighth international conference on Information and knowledge management

November 1999

564 pages

ISBN:1581131461

DOI:10.1145/319950

Editor:
Susan Gauch

Copyright © 1999 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 November 1999

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November 2 - 6, 1999

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Cited By

Esnaola-Gonzalez IBermúdez JFernandez IArnaiz A(2021)EEPSA as a core ontology for energy efficiency and thermal comfort in buildingsApplied Ontology10.3233/AO-21024516:2(193-228)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/AO-210245
Charfi MGripay YPetit JDesai BToyama MBernardino J(2016)Optimization of a Class of Temporal QueriesProceedings of the 20th International Database Engineering & Applications Symposium10.1145/2938503.2938514(346-351)Online publication date: 11-Jul-2016
https://dl.acm.org/doi/10.1145/2938503.2938514
Yahya BSong MBae HSul SWu J(2016)Domain-driven actionable process model discoveryComputers and Industrial Engineering10.1016/j.cie.2016.05.01099:C(382-400)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.cie.2016.05.010
Yue XCao LMiao DChen YXu B(2015)Multi-view attribute reduction model for traffic bottleneck analysisKnowledge-Based Systems10.1016/j.knosys.2015.03.02286:C(1-10)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.knosys.2015.03.022
Yue XChen YQian JZhong C(2014)Attributes reduction model with user preferences2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference10.1109/ITAIC.2014.7065033(191-196)Online publication date: Dec-2014
https://doi.org/10.1109/ITAIC.2014.7065033
Alnoukari MEl Sheikh A(2012)Knowledge Discovery Process ModelsBusiness Intelligence and Agile Methodologies for Knowledge-Based Organizations10.4018/978-1-61350-050-7.ch004(72-100)Online publication date: 2012
https://doi.org/10.4018/978-1-61350-050-7.ch004
Cao LZhao YZhang HLuo DZhang CPark E(2010)Flexible Frameworks for Actionable Knowledge DiscoveryIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2009.14322:9(1299-1312)Online publication date: 1-Sep-2010
https://dl.acm.org/doi/10.1109/TKDE.2009.143
Pan D(2010)A Continuous Knowledge Discovery Framework with Time GranularityProceedings of the 2010 Third International Symposium on Information Processing10.1109/ISIP.2010.102(355-359)Online publication date: 15-Oct-2010
https://dl.acm.org/doi/10.1109/ISIP.2010.102
Behja HZemmouri EMarzak A(2010)Viewpoint-based annotations for Knowledge Discovery in Databases2010 International Conference on Machine and Web Intelligence10.1109/ICMWI.2010.5647881(320-323)Online publication date: Oct-2010
https://doi.org/10.1109/ICMWI.2010.5647881
Cao L(2009)Actionable Knowledge DiscoveryEncyclopedia of Information Science and Technology, Second Edition10.4018/978-1-60566-026-4.ch002(8-13)Online publication date: 2009
https://doi.org/10.4018/978-1-60566-026-4.ch002
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