Knowledge Engineering with Rules

Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 130)

Abstract

Intelligent systems that use knowledge representation and reasoning methods are commonly referred to as knowledge-based systems. The domain that considers building them is most generally referred to as knowledge engineering. This chapter introduces some of the important knowledge engineering topics with respect to rule-based systems. We discuss the modeling of acquired knowledge with the use of rules and other representations supporting the design. Once a rule set is built, an inference mechanism should be considered. In the case of large rule sets their structure has to be considered. The quality of the rule base should be analyzed during the design, or at least after it. If possible, the rule base should be kept independent of the specific system implementation. To make this possible a specific rule interchange method can be used. Finally, as today rule-based systems are not usually stand-alone systems, specific architectures for their integration are discussed.

References

  1. 1.
    Guida, G., Tasso, C.: Design and Development of Knowledge-Based Systems: From Life Cycle to Methodology. Wiley, New York (1995)MATHGoogle Scholar
  2. 2.
    Gonzalez, A.J., Dankel, D.D.: The Engineering of Knowledge-Based Systems: Theory and Practice. Prentice-Hall Inc, Upper Saddle River (1993)Google Scholar
  3. 3.
    Durkin, J.: Expert Systems: Design and Development. Macmillan, New York (1994)MATHGoogle Scholar
  4. 4.
    Waterman, D.A.: A Guide to Expert Systems. Addison-Wesley Longman Publishing Co., Inc, Boston (1985)Google Scholar
  5. 5.
    Liebowitz, J. (ed.): The Handbook of Applied Expert Systems. CRC Press, Boca Raton (1998)Google Scholar
  6. 6.
    Sommerville, I.: Software Engineering. International Computer Science, 7th edn. Pearson Education Limited, Harlow (2004)MATHGoogle Scholar
  7. 7.
    Hayes-Roth, F., Waterman, D.A., Lenat, D.B.: Building Expert Systems. Addison-Wesley Longman Publishing Co., Inc, Boston (1983)Google Scholar
  8. 8.
    Buchanan, B.G., Shortliffe, E.H. (eds.): Rule-Based Expert Systems. Addison-Wesley Publishing Company, Reading (1985)Google Scholar
  9. 9.
    von Halle, B.: Business Rules Applied: Building Better Systems Using the Business Rules Approach. Wiley, New York (2001)Google Scholar
  10. 10.
    Scott, A.C.: A Practical Guide to Knowledge Acquisition, 1st edn. Addison-Wesley Longman Publishing Co., Inc, Boston (1991)MATHGoogle Scholar
  11. 11.
    van Harmelen, F., Lifschitz, V., Porter, B. (eds.) Handbook of Knowledge Representation. Elsevier Science, Amsterdam (2007)Google Scholar
  12. 12.
    Stefik, M.: Introduction to Knowledge Systems. Morgan Kaufmann Publishers, San Francisco (1995)Google Scholar
  13. 13.
    Buchanan, B.G., Wilkins, D.C.: Readings in Knowledge Acquisition and Learning: Automating the Construction and Improvement of Expert Systems. M. Kaufmann Publishers, San Francisco (1993)MATHGoogle Scholar
  14. 14.
    Debenham, J.: Knowledge Engineering: Unifying Knowledge Base and Database Design, 1st edn. Springer Publishing Company, Incorporated, Berlin (2012)MATHGoogle Scholar
  15. 15.
    Nalepa, G.J.: Languages and tools for rule modeling. In: Giurca, A., Gašević, D., Taveter, K. (eds.) Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches, pp. 596–624. IGI Global, Hershey (2009)Google Scholar
  16. 16.
    Ligęza, A.: Logical Foundations for Rule-Based Systems. Springer, Berlin (2006)MATHGoogle Scholar
  17. 17.
    Ben-Ari, M.: Mathematical Logic for Computer Science. Springer, London (2001)CrossRefMATHGoogle Scholar
  18. 18.
    Ignizio, J.P.: An Introduction to Model Driven Architecture. Applying MDA to Enterprise Computing. David S. Frankel, Wiley Publishing, Inc., Indianapolis 2003 Expert Systems. The Development and Implementation of Rule-Based Expert Systems. McGraw-Hill, New York (1991)Google Scholar
  19. 19.
    Graham, I.: Business Rules Management and Service Oriented Architecture. Wiley, New York (2006)Google Scholar
  20. 20.
    Jackson, P.: Introduction to Expert Systems, 3rd edn. Addison–Wesley, Boston (1999). ISBN 0-201-87686-8Google Scholar
  21. 21.
    Giarratano, J., Riley, G.: Expert Systems. Principles and Programming, 4th edn. Thomson Course Technology, Boston (2005). ISBN 0-534-38447-1Google Scholar
  22. 22.
    Sutherland, G.R.: DENDRAL, a computer program for generating and filtering chemical structures. Report Memo AI-49, Stanford University, Department of Computer Science, Stanford, California (1967)Google Scholar
  23. 23.
    Buchanan, B.G., Shortliffe, E.H.: Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project. The Addison-Wesley Series in Artificial Intelligence. Addison-Wesley Longman Publishing Co. Inc., Boston (1984)Google Scholar
  24. 24.
    Forgy, C.: Rete: a fast algorithm for the many patterns/many objects match problem. Artif. Intell. 19(1), 17–37 (1982)CrossRefGoogle Scholar
  25. 25.
    Miranker, D.P.: TREAT: a better match algorithm for AI production systems; long version. Technical report 87-58, University of Texas (1987)Google Scholar
  26. 26.
    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
  27. 27.
    Kaczor, K., Nalepa, G.J., Bobek, S.: Rule modularization and inference solutions – a synthetic overview. In: Schneider, A. (ed.) Crossing Borders within ABC. Automation, Biomedical Engineering and Computer Science: 55 IWK Internationales Wissenschaftliches Kolloquium: International Scientific Colloquium, Illmenau, Germany, pp. 555–560 (2010)Google Scholar
  28. 28.
    Bobek, S., Kaczor, K., Nalepa, G.J.: Overview of rule inference algorithms for structured rule bases. Gdansk Univ. Technol. Fac. ETI Ann. 18(8), 57–62 (2010)Google Scholar
  29. 29.
    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 (2011)Google Scholar
  30. 30.
    Vanthienen, J., Dries, E., Keppens, J.: Clustering knowledge in tabular knowledge bases. In: ICTAI, pp. 88–95 (1996)Google Scholar
  31. 31.
    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)CrossRefGoogle Scholar
  32. 32.
    Nalepa, G.J.: Semantic Knowledge Engineering. A Rule-Based Approach. Wydawnictwa AGH, Kraków (2011)Google Scholar
  33. 33.
    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)CrossRefGoogle Scholar
  34. 34.
    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)Google Scholar
  35. 35.
    Nazareth, D.L.: Issues in the verification of knowledge in rule-based systems. Int. J. Man-Mach. Stud. 30(3), 255–271 (1989)CrossRefGoogle Scholar
  36. 36.
    Preece, A.D.: Verification, validation, and test of knowledge-based systems. AI Mag. 13(4), 77 (1992)Google Scholar
  37. 37.
    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)CrossRefGoogle Scholar
  38. 38.
    Preece, A.D., Shinghal, R.: Foundation and application of knowledge base verification. Int. J. Intell. Syst. 9(8), 249–269 (1994)CrossRefGoogle Scholar
  39. 39.
    Meseguer, P.: Incremental verification of rule-based expert systems. In: Proceedings of the 10th European Conference on Artificial Intelligence. ECAI’92. Wiley, New York, NY, USA, pp. 840–844 (1992)Google Scholar
  40. 40.
    Suwa, M., Scott, C.A., Shortliffe, E.H.: Completeness and consistency in rule-based expert system. Rule-Based Expert Systems, pp. 159–170. Addison-Wesley Publishing Company, Reading (1985)Google Scholar
  41. 41.
    Tepandi, J.: Verification, testing, and validation of rule-based expert systems. In: Proceedings of the 11-th IFAC World Congress, pp. 162–167 (1990)Google Scholar
  42. 42.
    Szpyrka, M.: Design and analysis of rule-based systems with adder designer. In: Cotta, C., Reich, S., Schaefer, R., Ligęza, A. (eds.) Knowledge-Driven Computing: Knowledge Engineering and Intelligent Computations. Studies in Computational Intelligence, vol. 102, pp. 255–271. Springer, Berlin (2008)Google Scholar
  43. 43.
    Boehm, B.W.: Verifying and validating software requirements and design specifications. IEEE Softw. 1(1), 75–88 (1984)CrossRefGoogle Scholar
  44. 44.
    Vermesan, A.I., Coenen, F. (eds.) Validation and Verification of Knowledge Based Systems. Theory, Tools and Practice. Kluwer Academic Publisher, Boston (1999)Google Scholar
  45. 45.
    Wentworth, J.A., Knaus, R., Aougab, H.: Verification, Validation and Evaluation of Expert Systems. World Wide Web Electronic Publication. http://www.tfhrc.gov/advanc/vve/cover.htm
  46. 46.
    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. Springer, Berlin, pp. 6–16 (2008)Google Scholar
  47. 47.
    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 (2009)Google Scholar
  48. 48.
    Tsai, W.T., Vishnuvajjala, R., Zhang, D.: Verification and validation of knowledge-based systems. IEEE Trans. Knowl. Data Eng. 11, 202–212 (1999)Google Scholar
  49. 49.
    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)Google Scholar
  50. 50.
    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, New York (1991)Google Scholar
  51. 51.
    Preece, A.D., Shinghal, R., Batarekh, A.: Principles and practice in verifying rule-based systems. Knowl. Eng. Rev. 7(02), 115–141 (1992)CrossRefGoogle Scholar
  52. 52.
    Zhang, D., Nguyen, D.: Prepare: a tool for knowledge base verification. IEEE Trans. Knowl. Data Eng. 6(6), 983–989 (1994)CrossRefGoogle Scholar
  53. 53.
    Ayel, M., Laurent, J.P.: Sacco-Sycojet: two different ways of verifying knowledge-based systems. In: Ayel, M., Laurent, J.P. (eds.) Validation, Verification and Test of Knowledge-Based Systems, pp. 63–76. Wiley, New York (1991)Google Scholar
  54. 54.
    Rousset, M.C.: On the consistency of knowledge bases: the covadis system. In: ECAI, pp. 79–84 (1988)Google Scholar
  55. 55.
    Craw, S., Sleeman, D.H.: Automating the refinement of knowledge-based systems. In: ECAI, pp. 167–172 (1990)Google Scholar
  56. 56.
    Culbert, S.: Expert system verifications and validation. In: Proceedings of First AAAI Workshop on V,V and Testing (1988)Google Scholar
  57. 57.
    Preece, A.D.: A new approach to detecting missing knowledge in expert system rule bases. Int. J. Man-Mach. Stud. 38, 161–181 (1993)CrossRefGoogle Scholar
  58. 58.
    Vermesan, A.: Foundation and application of expert system verification and validation. The Handbook of Applied Expert Systems. CRC Press, Boca Raton (1997)Google Scholar
  59. 59.
    Genesereth, M.R., Fikes, R.E.: Knowledge interchange format version 3.0 reference manual (1992)Google Scholar
  60. 60.
    Delugach, H.: ISO/IEC 24707 information technology–common logic (CL) – A framework for a family of logic-based languages. The formally adopted ISO specification (2007)Google Scholar
  61. 61.
    Kifer, M., Boley, H.: RIF overview. W3C working draft, W3C (2009). http://www.w3.org/TR/rif-overview
  62. 62.
    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, October 31–November 1, 2008. Proceedings. Lecture Notes in Computer Science, vol. 5341, pp. 1–11. Springer (2008)Google Scholar
  63. 63.
    Paschke, A., Reynolds, D., Hallmark, G., Boley, H., Kifer, M., Polleres, A.: RIF core dialect. Candidate recommendation, W3C (2009). http://www.w3.org/TR/2009/CR-rif-core-20091001/
  64. 64.
    Kifer, M., Boley, H.: RIF basic logic dialect. Candidate recommendation, W3C (2009). http://www.w3.org/TR/2009/CR-rif-bld-20091001/
  65. 65.
    Hallmark, G., Paschke, A., de Sainte Marie, C.: RIF production rule dialect. Candidate recommendation, W3C (2009). http://www.w3.org/TR/2009/CR-rif-prd-20091001/
  66. 66.
    Lukichev, S.: Towards rule interchange and rule verification. Ph.D. thesis, Brandenburg University of Technology (2010) urn:nbn:de:kobv:co1-opus-20772. http://d-nb.info/1013547209
  67. 67.
    Wang, X., Ma, Z.M., Zhang, F., Yan, L.: RIF centered rule interchange in the semantic web. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) Database and Expert Systems Applications, 21st International Conference, DEXA 2010, Bilbao, Spain, August 30–September 3, 2010, Proceedings, Part I. Lecture Notes in Computer Science, vol. 6261, pp. 478–486. Springer (2010)Google Scholar
  68. 68.
    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
  69. 69.
    Boley, H., Paschke, A., Shafiq, O.: RuleML 1.0: the overarching specification of web rules. In: Dean, M., Hall, J., Rotolo, A., Tabet, S. (eds.) Semantic Web Rules - International Symposium, RuleML 2010, Washington, DC, USA, 21–23 October 2010. Proceedings. Lecture Notes in Computer Science, vol. 6403, pp. 162–178. Springer (2010)Google Scholar
  70. 70.
    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, July 30–August 1, 2001, pp. 381–401 (2001)Google Scholar
  71. 71.
    Wagner, G., Antoniou, G., Tabet, S., Boley, H.: The abstract syntax of RuleML - towards a general web rule language framework. Web Intell. IEEE Comput. Soc. 628–631 (2004)Google Scholar
  72. 72.
    Wagner, G., Giurca, A., Lukichev, S.: R2ml: a general approach for marking up rules. In: Bry, F., Fages, F., Marchiori, M., Ohlbach, H. (eds.) Principles and Practices of Semantic Web Reasoning, Dagstuhl Seminar Proceedings 05371 (2005)Google Scholar
  73. 73.
    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)MATHGoogle Scholar
  74. 74.
    Giarratano, J.C., Riley, G.D.: Expert Systems. Thomson, Toronto (2005)Google Scholar
  75. 75.
    Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns, 1st edn. Addison-Wesley Publishing Company, Reading (1995)Google Scholar
  76. 76.
    Miller, J., Mukerji, J.: MDA guide version 1.0.1. OMG (2003)Google Scholar
  77. 77.
    Mellor, S.J., Balcer, M.J.: Executable UML: A Foundation for Model Driven Architecture, 1st edn. Addison-Wesley Professional, Boston (2002)Google Scholar
  78. 78.
    Burbeck, S.: Applications programming in smalltalk-80(TM): How to use model-view-controller (MVC). Technical report, Department of Computer Science, University of Illinois, Urbana-Champaign (1992)Google Scholar
  79. 79.
    Bieberstein, N., Bose, S., Fiammante, M., Jones, K., Shah, R.: Service-Oriented Architecture (SOA) Compass: Business Value, Planning, and Enterprise Roadmap. IBM Press, Indianapolis (2006)Google Scholar
  80. 80.
    OMG: Business process model and notation (BPMN): version 2.0 specification. Technical report formal/2011-01-03, Object Management Group (2011)Google Scholar
  81. 81.
    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, April 29–May 3, 2012. Lecture Notes in Artificial Intelligence, vol. 7268, pp. 573–581. Springer (2012)Google Scholar
  82. 82.
    Kluza, K., Nalepa, G.J.: Business processes integrated with business rules, F.M. Inf. Syst. Front. (2016) submittedGoogle Scholar
  83. 83.
    Guida, G., Lamperti, G., Zanella, M.: Software Prototyping in Data and Knowledge Engineering. Kluwer Academic Publishers, Norwell (1999)CrossRefMATHGoogle Scholar
  84. 84.
    Kluza, K., Nalepa, G.J., Lisiecki, J.: Square complexity metrics for business process models. In: Mach-Król, M., Pełch-Pilichowski, T. (eds.) Advances in Business ICT. Advances in Intelligent Systems and Computing, vol. 257, pp. 89–107. Springer, Berlin (2014)Google Scholar

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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakówPoland

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