Abstract
The rule knowledge-based systems are still popular in the real-world applications and the rules are considered as a standard form of knowledge representation in intelligent information systems. While the number of knowledge-based applications grows, the number of tools for building such systems grows much more slowly. This work is the part of research focused on the development of new methods and tools for building rule-based expert systems. The software components mentioned in this work are the main parts of the distributed expert system shell. The realized implementation assumes, that the inference is performed on the preloaded knowledge base stored in the memory. But such a way of using rule bases may be unrealisable or ineffective for large ones, especially when a weak hardware configuration (mobile applications, embedded systems) is used. In this work the utilization of a database stored procedures is considered. This approach minimizes the network traffic and is independent from the used programming tools—only a connection to the database server is required. The main goal of the experiments was to describe an experimental implementation of the forward chaining inference algorithm (as the stored procedure) and to evaluate this approach in comparison to performing inference on preloaded (real-world) knowledge bases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Acquired Intelligence: Acquired Intelligence Home Page. http://aiinc.ca. Accessed Oct 2015
Akerkar, R., Sajja, P.: Knowledge-Based Systems. Jones and Bartlett Publishers, Burlington (2010)
Canadas, J., Palma, J., Túnez, S.: A tool for MDD of rule-based web applications based on OWL and SWRL. In: Knowledge Engineering and Software Engineering (KESE6), p. 1 (2010)
CLIPS: CLIPS NASA Home Page. http://www.siliconvalleyone.com/founder/clips/index.htm. Accessed Nov 2016
DROOLS: DROOLS Home Page. https://www.drools.org. Accessed Nov 2016
Duan, Y., Edwards, J.S., Xu, M.: Web-based expert systems: benefits and challenges. Inf. Manag. 42(6), 799–811 (2005)
eXpertise2Go: eXpertise2Go Home Page. http://expertise2go.com. Accessed Nov 2016
Exsys: Exsys Home Page. http://www.exsys.com. Accessed Nov 2016
Gensym Corporation: Gensym Corporation Announces Gensym G2 8.4R2 Platform. http://www.marketwired.com. Accessed Jan 2017
Grove, R.: Internet-based expert systems. Expert Syst. 17(3), 129–135 (2000)
Grzymala-Busse, J.W.: Managing Uncertainty in Expert Systems, vol. 143. Springer Science & Business Media, New York (2012)
Huntington, D.: Web-based expert systems are on the way: Java-based web delivery. PC AI 14(6), 34–36 (2000)
Jach, T., Xięski, T.: Inference in expert systems using natural language processing. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015. CCIS, vol. 521, pp. 288–298. Springer, Cham (2015). doi:10.1007/978-3-319-18422-7_26
JESS: JESS Information. http://herzberg.ca.sandia.gov. Accessed Nov 2016
Li, D., Fu, Z., Duan, Y.: Fish-expert: a web-based expert system for fish disease diagnosis. Expert Syst. Appl. 23(3), 311–320 (2002)
Ligeza, A.: Logical Foundations for Rule-based Systems, vol. 11. Springer, Heidelberg (2006)
Ligeza, A., Nalepa, G.J.: Knowledge representation with granular attributive logic for XTT-based expert systems. In: FLAIRS Conference, pp. 530–535 (2007)
Mathkour, H., Al-Turaiki, I., Touir, A.: The development of a bilingual fuzzy expert system shell. J. King Saud Univ.-Comput. Inf. Sci. 21, 27–44 (2009)
Nowak-Brzezinska, A., Siminski, R.: New inference algorithms based on rules partition. In: Proceedings of the 23th International Workshop on Concurrency, Specification and Programming, Chemnitz, Germany, 29 September–1 October, 2014, pp. 164–175 (2014). http://ceur-ws.org/Vol-1269/paper164.pdf
Simiński, R., Nowak-Brzezińska, A.: Goal-driven inference for web knowledge based system. In: Wilimowska, Z., Borzemski, L., Grzech, A., Świątek, J. (eds.) Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology – ISAT 2015 – Part IV. AISC, vol. 432, pp. 99–109. Springer, Cham (2016). doi:10.1007/978-3-319-28567-2_9
Polkowski, L.: Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, vol. 19. Physica, Heidelberg (2013)
Ruiz-Mezcua, B., Garcia-Crespo, A., Lopez-Cuadrado, J., Gonzalez-Carrasco, I.: An expert system development tool for non AI experts. Expert Syst. Appl. 38(1), 597–609 (2011)
Sajja, P.S., Akerkar, R.: Knowledge-based systems for development. Adv. Knowl. Based Syst.: Model Appl. Res. 1, 1–11 (2010)
Simiński, R.: Extraction of rules dependencies for optimization of backward inference algorithm. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2014. CCIS, vol. 424, pp. 191–200. Springer, Cham (2014). doi:10.1007/978-3-319-06932-6_19
Simiński, R.: The kbexpertlib software library for java-functionality properties and performance study. Studia Inform. 37(1), 125–134 (2016)
Simiński, R.: Multivariate approach to modularization of the rule knowledge bases. In: Gruca, A., Brachman, A., Kozielski, S., Czachórski, T. (eds.) Man–Machine Interactions 4. AISC, vol. 391, pp. 473–483. Springer, Cham (2016). doi:10.1007/978-3-319-23437-3_40
Simiński, R.: The experimental evaluation of rules partitioning conception for knowledge base systems. In: Borzemski, L., Grzech, A., Świątek, J., Wilimowska, Z. (eds.) Information Systems Architecture and Technology: Proceedings of 37th International Conference on Information Systems Architecture and Technology – ISAT 2016 – Part I. AISC, vol. 521, pp. 79–89. Springer, Cham (2017). doi:10.1007/978-3-319-46583-8_7
Simiński, R., Nowak-Brzezińska, A.: KBExplorator and KBExpertLib as the tools for building medical decision support systems. In: Nguyen, N.-T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) ICCCI 2016. LNCS (LNAI), vol. 9876, pp. 494–503. Springer, Cham (2016). doi:10.1007/978-3-319-45246-3_47
Siminski, R., Wakulicz-Deja, A.: Rough sets inspired extension of forward inference algorithm. In: Proceedings of the 24th International Workshop on Concurrency, Specification and Programming, Rzeszow, Poland, 28–30 September 2015, vol. 2, pp. 161–172 (2015)
Simiński, R., Xiȩski, T.: Physical knowledge base representation for web expert system shell. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015-2016. CCIS, vol. 613, pp. 558–570. Springer, Cham (2016). doi:10.1007/978-3-319-34099-9_43
SPHINX: SPHINX Home Page. https://aitech.pl. Accessed Nov 2016
Wang, W., Yang, M., Seong, P.H.: Development of a rule-based diagnostic platform on an object-oriented expert system shell. Ann. Nucl. Energy 88, 252–264 (2016)
Xpert Rule: Xpert Rule Home Page. http://www.xpertrule.com. Accessed Nov 2016
Zetian, F., Feng, X., Yun, Z., XiaoShuan, Z.: Pig-vet: a web-based expert system for pig disease diagnosis. Expert Syst. Appl. 29(1), 93–103 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Xiȩski, T., Simiński, R. (2017). A Performance Study of Two Inference Algorithms for a Distributed Expert System Shell. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation. BDAS 2017. Communications in Computer and Information Science, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-319-58274-0_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-58274-0_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58273-3
Online ISBN: 978-3-319-58274-0
eBook Packages: Computer ScienceComputer Science (R0)