Skip to main content
SpringerLink
Log in
Book cover

Advances in Data Analysis with Computational Intelligence Methods pp 203–223Cite as

Techniques for Construction and Integration of Rule Bases

  • Chapter
  • First Online:

  • 1211 Accesses

Part of the book series: Studies in Computational Intelligence ((SCI,volume 738))

Abstract

This chapter discusses issues in the practical integration approaches for intelligent rule-based systems. In it selected issues that need to be addressed for performing integration of rule based systems are identified and discussed. These include high level modeling techniques for rule bases, integration architectures for rule-based systems, and rule interoperability challenges. In the chapter a short review of different rule types and languages used to express them is given. Moreover, important issues regarding construction of complex rule bases are introduced. Furthermore, the execution issues of rule bases are considered, with the emphasis on addressing the structure identified during modeling. Finally, main approaches to integration and interoperability of rule-based systems are given.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    See http://www.ruleml.org.

References

  1. Andert, E.P.: Integrated knowledge-based system design and validation for solving problems in uncertain environments. Int. J. Man-Mach. Stud. 36(2), 357–373 (1992). http://www.reviews.com/reviewer/quickreview/frameset_toplevel.cfm?bib_id=144453

  2. Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press (2003)

    Google Scholar 

  3. Bieberstein, N., Bose, S., Fiammante, M., Jones, K., Shah, R.: Service-Oriented Architecture (SOA) Compass: Business Value, Planning, and Enterprise Roadmap. IBM Press (2006)

    Google Scholar 

  4. Boehm, B.W.: Verifying and validating software requirements and design specifications. IEEE Softw. 1(1), 75–88 (1984)

    Article  Google Scholar 

  5. Boley, H., Paschke, A., Shafiq, O.: RuleML 1.0: the overarching specification of web rules. In: M. Dean, J. Hall, A. Rotolo, S. Tabet (eds.) Semantic Web Rules—International Symposium, RuleML 2010, Washington, DC, USA, 21–23 Oct 2010. Proceedings. Lecture Notes in Computer Science, vol. 6403, pp. 162–178. Springer (2010). doi:10.1007/978-3-642-16289-3

  6. Boley, H., Tabet, S., Wagner, G.: Design rationale for RuleML: a markup language for semantic web rules. In: Cruz, I.F., Decker, S., Euzenat, J., McGuinness, D.L. (eds.) Proceedings of SWWS’01, The First Semantic Web Working Symposium, Stanford University, California, USA, 30 July–1 Aug 2001, pp. 381–401 (2001). http://www.semanticweb.org/SWWS/program/full/paper20.pdf

  7. Brachman, R., Levesque, H.: Knowledge Representation and Reasoning, 1st edn. Morgan Kaufmann (2004)

    Google Scholar 

  8. Browne, P.: JBoss Drools Business Rules. Packt Publishing (2009)

    Google Scholar 

  9. Burbeck, S.: Applications programming in Smalltalk-80(TM): How to use Model-View-Controller (MVC). Department of Computer Science, University of Illinois, Urbana-Champaign, Technical report (1992)

    Google Scholar 

  10. Charles, E., Dubois, O.: Melodia: logical methods for checking knowledge bases. In: Ayel, M., Laurent, J.P. (eds.) Validation, Verification and Test of Knowledge-Based Systems, pp. 95–105. Wiley, New York (1991). http://portal.acm.org/citation.cfm?id=130251.130258

  11. Chorowski, J., Zurada, J.M.: Extracting rules from neural networks as decision diagrams. IEEE Trans. Neural Netw. 22(12), 2435–2446 (2011). doi:10.1109/TNN.2011.2106163

  12. Community, J.: Drools verifier. http://community.jboss.org/wiki/DroolsVerifier (2009)

  13. Connolly, T., Begg, C., Strechan, A.: Database Systems, A Practical Approach to Design, Implementation, and Management, 2nd edn. Addison-Wesley (1999)

    Google Scholar 

  14. Culbert, S.: Expert system verifications and validation. In: Proceedings of First AAAI Workshop on V,V & Testing, Aug 1988

    Google Scholar 

  15. De Raedt, L., Sablon, G., Bruynooghe, M.: Using interactive concept-learning for knowledge base validation and verification. In: Ayel, M., Laurent, J. (eds.) Validation, Verification and Testing of Knowledge Based Systems, pp. 177–190. Wiley (1991)

    Google Scholar 

  16. Duch, W., Setiono, R., Zurada, J.M.: Computational intelligence methods for rule-based data understanding. In: Proceedings of the IEEE, pp. 771–805 (2004)

    Google Scholar 

  17. Flach, P.: Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge University Press, New York (2012)

    Book  MATH  Google Scholar 

  18. Forgy, C.: Rete: a fast algorithm for the many patterns/many objects match problem. Artif. Intell. 19(1), 17–37 (1982)

    Article  Google Scholar 

  19. Friedman-Hill, E.: Jess in Action, Rule Based Systems in Java. Manning (2003)

    Google Scholar 

  20. Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns, 1st edn. Addison-Wesley Pub Co. (1995)

    Google Scholar 

  21. Genesereth, M.R., Fikes, R.E.: Knowledge Interchange Format Version 3.0 Reference Manual (1992)

    Google Scholar 

  22. Giarratano, J., Riley, G.: Expert Systems. Principles and Programming, 4th edn. Thomson Course Technology, Boston, MA, United States (2005). ISBN 0-534-38447-1

    Google Scholar 

  23. Giarratano, J.C., Riley, G.D.: Expert Systems. Thomson (2005)

    Google Scholar 

  24. Giurca, A., Gašević, D., Taveter, K. (eds.): Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches. Information Science Reference, Hershey, New York (2009)

    Google Scholar 

  25. Graham, I.: Business Rules Management and Service Oriented Architecture. Wiley (2006)

    Google Scholar 

  26. von Halle, B.: Business Rules Applied: Building Better Systems Using the Business Rules Approach. Wiley (2001)

    Google Scholar 

  27. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publisher (2000)

    Google Scholar 

  28. Hanson, E.N., Hasan, M.S.: Gator: An Optimized Discrimination Network for Active Database Rule Condition Testing. Technical Report 93-036, CIS Department University of Florida (1993)

    Google Scholar 

  29. van Harmelen, F., Lifschitz, V., Porter, B. (eds.): Handbook of Knowledge Representation. Elsevier Science (2007)

    Google Scholar 

  30. Hayes-Roth, B.: A blackboard architecture for control. Artif. Intell. 26(3), 251–321 (1985)

    Article  Google Scholar 

  31. Herre, H., Jaspars, J.O.M., Wagner, G.: Partial logics with two kinds of negation as a foundation for knowledge-based reasoning. Centrum voor Wiskunde en Informatica (CWI) 158, 35 (1995)

    Google Scholar 

  32. Jackson, P.: Introduction to Expert Systems, 3rd edn. Addison-Wesley (1999). ISBN 0-201-87686-8

    Google Scholar 

  33. Kacprzyk, J., Pedrycz, W. (eds.): Springer Handbook of Computational Intelligence. Springer (2015). doi:10.1007/978-3-662-43505-2

  34. Kifer, M.: Rule interchange format: the framework. In: Calvanese, D., Lausen, G. (eds.) Web Reasoning and Rule Systems, Second International Conference, RR 2008, Karlsruhe, Germany, 31 Oct–1 Nov 2008. Proceedings. Lecture Notes in Computer Science, vol. 5341, pp. 1–11. Springer (2008). doi:10.1007/978-3-540-88737-9_1

  35. Kifer, M., Boley, H.: RIF overview. W3C working draft, W3C. http://www.w3.org/TR/rif-overview (2009)

  36. Kifer, M., Lausen, G., Wu, J.: Logical foundations of object-oriented and frame-based languages. J. ACM 42(4), 741–843 (1995). doi:10.1145/210332.210335

  37. Klösgen, W., Żytkow, J.M. (eds.): Handbook of Data Mining and Knowledge Discovery. Oxford University Press, New York (2002)

    MATH  Google Scholar 

  38. Kluza, K., Kaczor, K., Nalepa, G.J.: Enriching business processes with rules using the Oryx BPMN editor. In: Rutkowski, L., et al. (eds.) Artificial Intelligence and Soft Computing: 11th International Conference, ICAISC 2012: Zakopane, Poland, 29 Apr–3 May 2012. Lecture Notes in Artificial Intelligence, vol. 7268, pp. 573–581. Springer (2012). http://www.springerlink.com/content/u654r0m56882np77/

  39. Liebowitz, J. (ed.): The Handbook of Applied Expert Systems. CRC Press, Boca Raton (1998)

    MATH  Google Scholar 

  40. Ligęza, A.: Logical Foundations for Rule-Based Systems. Springer, Berlin, Heidelberg (2006)

    MATH  Google Scholar 

  41. Ligęza, A., Nalepa, G.J.: Rules verification and validation. In: Giurca, A., Gašević, D., Taveter, K. (eds.) Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches, pp. 273–301. IGI Global, Hershey, New York (2009)

    Chapter  Google Scholar 

  42. Mellor, S.J., Balcer, M.J.: Executable UML: A Foundation for Model Driven Architecture, 1st edn. Addison-Wesley Professional (2002)

    Google Scholar 

  43. Meseguer, P.: Incremental verification of rule-based expert systems. In: Proceedings of the 10th European conference on Artificial intelligence, ECAI ’92, pp. 840–844. Wiley, New York, NY, USA (1992). http://portal.acm.org/citation.cfm?id=145448.147581

  44. Miller, J., Mukerji, J.: MDA Guide Version 1.0.1. OMG (2003)

    Google Scholar 

  45. Miranker, D.P.: TREAT: A Better Match Algorithm for AI Production Systems; Long Version. Technical Report 87-58, University of Texas (1987)

    Google Scholar 

  46. Nalepa, G., Bobek, S., Ligęza, A., Kaczor, K.: Algorithms for rule inference in modularized rule bases. In: Bassiliades, N., Governatori, G., Paschke, A. (eds.) Rule-Based Reasoning, Programming, and Applications. Lecture Notes in Computer Science, vol. 6826, pp. 305–312. Springer, Berlin, Heidelberg (2011)

    Chapter  Google Scholar 

  47. Nalepa, G., Bobek, S., Ligęza, A., Kaczor, K.: HalVA—rule analysis framework for XTT2 rules. In: Bassiliades, N., Governatori, G., Paschke, A. (eds.) Rule-Based Reasoning, Programming, and Applications. Lecture Notes in Computer Science, vol. 6826, pp. 337–344. Springer, Berlin, Heidelberg (2011). http://www.springerlink.com/content/c276374nh9682jm6/

  48. Nalepa, G.J.: Architecture of the HeaRT hybrid rule engine. In: Rutkowski, L., et al. (eds.) Artificial Intelligence and Soft Computing: 10th International Conference, ICAISC 2010: Zakopane, Poland, 13–17 June 2010, Pt. II. Lecture Notes in Artificial Intelligence, vol. 6114, pp. 598–605. Springer (2010)

    Google Scholar 

  49. Nalepa, G.J.: Loki—semantic wiki with logical knowledge representation. In: Nguyen, N.T. (ed.) Transactions on Computational Collective Intelligence III. Lecture Notes in Computer Science, vol. 6560, pp. 96–114. Springer (2011). http://www.springerlink.com/content/y91w134g03344376/

  50. Nalepa, G.J.: Semantic Knowledge Engineering. A Rule-Based Approach. Wydawnictwa AGH, Kraków (2011)

    Google Scholar 

  51. Nalepa, G.J., Kluza, K.: UML representation for rule-based application models with XTT2-based business rules. Int. J. Softw. Eng. Knowl. Eng. (IJSEKE) 22(4), 485–524 (2012). doi:10.1142/S021819401250012X, http://www.worldscientific.com

  52. Nalepa, G.J., Ligęza, A.: Conceptual modelling and automated implementation of rule-based systems. In: Software Engineering: Evolution and Emerging Technologies. Frontiers in Artificial Intelligence and Applications, vol. 130, pp. 330–340. IOS Press, Amsterdam (2005)

    Google Scholar 

  53. Nalepa, G.J., Ligęza, A.: HeKatE methodology, hybrid engineering of intelligent systems. Int. J. Appl. Math. Comput. Sci. 20(1), 35–53 (2010)

    Article  MATH  Google Scholar 

  54. Nalepa, G.J., Ligęza, A., Kaczor, K.: Formalization and modeling of rules using the XTT2 method. Int. J. Artif. Intell. Tools 20(6), 1107–1125 (2011)

    Article  Google Scholar 

  55. Nazareth, D.L.: Issues in the verification of knowledge in rule-based systems. Int. J. Man-Mach. Stud. 30(3), 255–271 (1989). http://www.reviews.com/reviewer/quickreview/frameset_toplevel.cfm?bib_id=69244

  56. Nguyen, M.N., Zurada, J.M., Rajapakse, J.C.: Toward better understanding of protein secondary structure: Extracting prediction rules. IEEE/ACM Trans. Comput. Biol. Bioinform. 8(3), 858–864 (2011). doi:10.1109/TCBB.2010.16

  57. Nguyen, T.A., Perkins, W.A., Laffey, T.J., Pecora, D.: Checking an expert systems knowledge base for consistency and completeness. In: IJCAI, pp. 375–378 (1985). http://dli.iiit.ac.in/ijcai/IJCAI-85-VOL1/PDF/070.pdf

  58. OMG: Production Rule Representation (OMG PRR) version 1.0 specification. Technical Report formal/2009-12-01, Object Management Group (2009). http://www.omg.org/spec/PRR/1.0

  59. OMG: Business Process Model and Notation (BPMN): Version 2.0 specification. Technical Report formal/2011-01-03, Object Management Group (2011)

    Google Scholar 

  60. Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht/Boston/London (1991)

    MATH  Google Scholar 

  61. Preece, A.D.: A new approach to detecting missing knowledge in expert system rule bases. Int. J. Man-Mach. Stud. 38(4), 661–688 (1993). http://users.cs.cf.ac.uk/A.D.Preece/publications/download/ijhcs1993.pdf

  62. Preece, A.D.: A new approach to detecting missing knowledge in expert system rule bases. Int. J. Man-Mach. Stud. 38, 161–181 (1993)

    Article  Google Scholar 

  63. Preece, A.D., Shinghal, R., Batarekh, A.: Principles and practice in verifying rule-based systems. Knowl. Eng. Rev. 7(02), 115–141 (1992). doi:10.1017/S026988890000624X

  64. Riley, G.: CLIPS—A Tool for Building Expert Systems. http://clipsrules.sourceforge.net (2008)

  65. Rousset, M.C.: On the consistency of knowledge bases: the COVADIS system. In: ECAI, pp. 79–84 (1988)

    Google Scholar 

  66. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: The CART decision tree for mining data streams. Inf. Sci. 266, 1–15 (2014). doi:10.1016/j.ins.2013.12.060

  67. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: Decision trees for mining data streams based on the Gaussian approximation. IEEE Trans. Knowl. Data Eng. 26(1), 108–119 (2014). doi:10.1109/TKDE.2013.34

  68. Salatino, M.: jBPM Developer Guide. Packt Publishing Ltd (2009)

    Google Scholar 

  69. Setiono, R., Leow, W.K., Zurada, J.M.: Extraction of rules from artificial neural networks for nonlinear regression. IEEE Trans. Neural Netw. 13(3), 564–577 (2002). doi:10.1109/TNN.2002.1000125

  70. Sommerville, I.: Software Engineering, 7th edn. International Computer Science. Pearson Education Limited (2004)

    Google Scholar 

  71. Tsai, W.T., Vishnuvajjala, R., Zhang, D.: Verification and validation of knowledge-based systems. IEEE Trans. Knowl. Data Eng. 11, 202–212 (1999). doi:10.1109/69.755629

  72. Vanthienen, J., Dries, E., Keppens, J.: Clustering knowledge in tabular knowledge bases. In: ICTAI, pp. 88–95 (1996)

    Google Scholar 

  73. Vermesan, A.I., Coenen, F. (eds.): Validation and Verification of Knowledge Based Systems. Theory, Tools and Practice. Kluwer Academic Publisher, Boston (1999)

    Google Scholar 

  74. Wagner, G., Antoniou, G., Tabet, S., Boley, H.: The abstract syntax of RuleML—towards a general web rule language framework. In: Web Intelligence, pp. 628–631. IEEE Computer Society (2004). doi:10.1109/WI.2004.134, http://doi.ieeecomputersociety.org

  75. Wagner, G., Damásio, C.V., Antoniou, G.: Towards a general web rule language. Int. J. Web Eng. Technol. 2(2/3), 181–206 (2005). doi:10.1504/IJWET.2005.008483

  76. Zacharias, V.: Development and verification of rule based systems—a survey of developers. In: Proceedings of the International Symposium on Rule Representation, Interchange and Reasoning on the Web, RuleML ’08, pp. 6–16. Springer, Berlin, Heidelberg (2008). doi:10.1007/978-3-540-88808-6_4

Download references

Author information

Authors and Affiliations

  1. AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059, Krakow, Poland

    Grzegorz J. Nalepa

Authors
  1. Grzegorz J. Nalepa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Grzegorz J. Nalepa .

Editor information

Editors and Affiliations

  1. Department of Medicine, Division of Nephrology and Hypertension, University of Louisville, Louisville, Kentucky, USA

    Adam E Gawęda

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Institute of Computational Intelligence, Department of Mechanical Engineering and Computer Science, Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  4. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, Oklahoma, USA

    Gary G. Yen

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Nalepa, G.J. (2018). Techniques for Construction and Integration of Rule Bases. In: Gawęda, A., Kacprzyk, J., Rutkowski, L., Yen, G. (eds) Advances in Data Analysis with Computational Intelligence Methods. Studies in Computational Intelligence, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-319-67946-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67946-4_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67945-7

  • Online ISBN: 978-3-319-67946-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation