Formalizing Interoperability in Rule Bases

  • Grzegorz J. NalepaEmail author
Part of the Intelligent Systems Reference Library book series (ISRL, volume 130)


With the increasing number of rules application areas, the number of different rule representations is also growing. As a result, rule-based knowledge cannot be easily shared among different rule bases. The goal of translation methods is to facilitate the process of interoperability between representations by providing an intermediate and formalized format for knowledge translation. We provide a definition of the formalized model for production rule representation. The proposed model is intended to be used as the intermediate format for rule interoperability between rule languages like CLIPS, Jess, Drools, or XTT. The discussed model is based on ALSV(FD) logic and significantly extends a formal model of XTT towards production rule systems. As we consider structured rule bases, module formalization is also introduced.


  1. 1.
    Winskel, G.: The Formal Semantics of Programming Languages: An Introduction. MIT Press, Cambridge (1993)zbMATHGoogle Scholar
  2. 2.
    Riley, G.: CLIPS - A Tool for Building Expert Systems (2008).
  3. 3.
    Friedman-Hill, E.: Jess in Action. Rule Based Systems in Java. Manning, Greenwich (2003)Google Scholar
  4. 4.
    Browne, P.: JBoss Drools Business Rules. Packt Publishing, Birmingham (2009)Google Scholar
  5. 5.
    Nalepa, G.J.: Semantic Knowledge Engineering. A Rule-Based Approach. Wydawnictwa AGH, Kraków (2011)Google Scholar
  6. 6.
    Kaczor, K., Nalepa, G.J., Łysik, Ł., Kluza, K.: Visual design of Drools rule bases using the XTT2 method. In: Katarzyniak, R., Chiu, T.F., Hong, C.F., Nguyen, N. (eds.) Semantic Methods for Knowledge Management and Communication. Studies in Computational Intelligence, vol. 381, pp. 57–66. Springer, Berlin (2011).
  7. 7.
    Kaczor, K., Kluza, K., Nalepa, G.J.: Towards rule interoperability: design of drools rule bases using the XTT2 method. Trans. Comput. Collect. Intell. XI 8065, 155–175 (2013)Google Scholar
  8. 8.
    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
  9. 9.
    Kaczor, K.: Knowledge formalization methods for semantic interoperability in rule bases. PhD thesis, AGH University of Science and Technology (2015) (Supervisor: Grzegorz J. Nalepa)Google Scholar
  10. 10.
    Ligęza, A.: Logical Foundations for Rule-Based Systems. Springer, Berlin (2006)zbMATHGoogle Scholar
  11. 11.
    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 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakówPoland

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