Knowledge discovery using maximization of the spread of influence in an expert system
Alexander Tselykh
Department of Information and Analytical Security Systems, Institute of Computer Technologies and Information Safety, Southern Federal University, Taganrog, Russia
Search for more papers by this authorCorresponding Author
Larisa Tselykh
Department of Economics and Business, Chekhov Taganrog Institute, Rostov State University of Economics, Taganrog, Russia
Correspondence
Larisa Tselykh, Department of Economics and Business, Chekhov Taganrog Institute, Rostov State University of Economics, Initsiativnaya, 48, Taganrog, Russia
Email: [email protected]
Search for more papers by this authorVladislav Vasilev
Department of Information and Analytical Security Systems, Institute of Computer Technologies and Information Safety, Southern Federal University, Taganrog, Russia
Search for more papers by this authorSimon Barkovskii
Department of Information and Analytical Security Systems, Institute of Computer Technologies and Information Safety, Southern Federal University, Taganrog, Russia
Search for more papers by this authorAlexander Tselykh
Department of Information and Analytical Security Systems, Institute of Computer Technologies and Information Safety, Southern Federal University, Taganrog, Russia
Search for more papers by this authorCorresponding Author
Larisa Tselykh
Department of Economics and Business, Chekhov Taganrog Institute, Rostov State University of Economics, Taganrog, Russia
Correspondence
Larisa Tselykh, Department of Economics and Business, Chekhov Taganrog Institute, Rostov State University of Economics, Initsiativnaya, 48, Taganrog, Russia
Email: [email protected]
Search for more papers by this authorVladislav Vasilev
Department of Information and Analytical Security Systems, Institute of Computer Technologies and Information Safety, Southern Federal University, Taganrog, Russia
Search for more papers by this authorSimon Barkovskii
Department of Information and Analytical Security Systems, Institute of Computer Technologies and Information Safety, Southern Federal University, Taganrog, Russia
Search for more papers by this authorAbstract
The problem of knowledge acquisition (KA) and the automation of this process is a bottleneck of expert systems (ESs) and remains a topical issue. The present study is an attempt to develop an ES in which inference attributes are formed on the basis of automated discovery of tacit knowledge. The structure of the KA module provides an opportunity to discover new knowledge from the cognitive representations of domain models. The algorithmic processing mechanism consists of three blocks: cluster analysis, influence analysis, and modelling of the equivalent graph. In the proposed expert system based on effective controls, a method of finding influences that highlights the main characteristics of the system is presented. These parameters are used as attributes for the further development of the rule base, expertise, or independent use in decision-making. A qualitative decrease in user subjectivity is achieved using this algorithm and through elimination of the knowledge engineer from the cycle of KA, which transforms it into a discovery knowledge cycle. Quality assurance of the knowledge base is achieved using three approaches: system presentation, mathematical support, and automation.
REFERENCES
- Akinnuwesi, B., Uzoka, F.-M., & Osamiluyi, A. (2013). Neuro-Fuzzy Expert System for evaluating the performance of Distributed Software System Architecture. Expert Systems with Applications, 40, 3313–3327. https://doi.org/10.1016/j.eswa.2012.12.039
- Akinnuwesi, B. A., Uzoka, F.-M. E., Olabiyisi, S. O., & Omidora, E. O. (2012). A framework for user-centric model for evaluating the performance of distributed software system architecture. Expert Systems with Applications, 39, 9323–9339. https://doi.org/10.1016/j.eswa.2012.02.067
- Altay, A., & Kayakutlu, G. (2011). Fuzzy cognitive mapping in factor elimination: A case study for innovative power and risks. WCIT-2010. Procedia Computer Science, 3, 1111–1119. Istanbul, Turkey. doi: https://doi.org/10.1016/j.procs.2010.12.181
- Amirkhani, A., Papageorgiou, E. I., Mohseni, A., & Mosavi, M. (2017). A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications. Computer Methods and Programs in Biomedicine, 142, 129–145.
- Breuker, J. (2013). A cognitive science perspective on knowledge acquisition. International Journal of Human-Computer Studies, 71, 177–183. https://doi.org/10.1016/j.ijhcs.2012.10.006
- Casillas, J., Cordón, O., Herrera, F., & Magdalena, L. (2003). Interpretability improvements to find the balance interpretability-accuracy in fuzzy modeling: An overview. In Interpretability issues in fuzzy modeling (Vol. 128) series Studies in Fuzziness and Soft Computing (pp. 3–22). Berlin: Springer.
10.1007/978-3-540-37057-4_1 Google Scholar
- Clancey, W. J. (1983). The epistemology of a rule based system—a framework for explanation. Artificial Intelligence, 20, 215–251. https://doi.org/10.1016/0004-3702 (83)90008-5
- Connolly, T., & Begg, C. (2002). Database systems: A practical approach to design, implementation, and management. Harlow, England: Addison-Wesley.
- Do Rosário, C. R., Kipper, L. M., Frozza, R., & Mariani, B. B. (2015). Modeling of tacit knowledge in industry: Simulations on the variables of industrial processes. Expert Systems with Applications, 145, 1613–1625. https://doi.org/10.1016/j.eswa. 2014.09.023
- Feigenbaum, E. (1977). The art of artificial intelligence: Themes and case studies of knowledge Engineering. In Proceedings of the Fifth International Joint Conference on Artificial Intelligence (47 (pp. 227–240). Los Altos: William Kaufmann.
10.21236/ADA046289 Google Scholar
- Ferreira, F., Jalali, M., & Ferreira, J. (2016). Integrating qualitative comparative analysis (QCA) and fuzzy cognitive maps (FCM) to enhance the selection of independent variables. Journal of Business Research, 69, 1471–1478. https://doi.org/10.1016/j.jbusres.2015.10.127
- Gomez, A., Morenoa, A., Parosa, J., & Sierra-Alonso, A. (2000). Knowledge maps: An essential technique for conceptualization. Data & Knowledge Engineering, 33, 169–190. https://doi.org/10.1016/S0169-023X(99)00050-6
- Guillaume, S., & Charnomordic, B. (2012). Fuzzy inference systems: An integrated modeling environment for collaboration between expert knowledge and data using FisPro. Expert Systems with Applications, 39, 8744–8755. https://doi.org/10.1016/j.eswa.2012.01.206
- Hayes-Roth, F., Waterman, D. A., & Lenat, D. B. (1983). Building expert systems. New York: Addison-Wesley.
- Kim, J. S. (2014). Development of a composite knowledge manipulation tool: K-Expert. Expert Systems with Applications, 41, 4337–4348. https://doi.org/10.1016/j.eswa.2014.01.009
- Kroenke, D., & Auer, D. J. (2012). Database processing: Fundamentals, design, and implementation. Boston et al. . USA: Pearson.
- Lee, K. C., & Lee, S. (2012). A causal knowledge-based expert system for planning an Internet-based stock trading system. Expert Systems with Applications, 39, 8526–8635. https://doi.org/10.1016/j.eswa.2012.01.191
- Leu, G., & Abbass, H. (2016). A multi-disciplinary review of knowledge acquisition methods: From human to autonomous eliciting agents. Knowledge-Based Systems, 105, 1–22. https://doi.org/10.1016/j.knosys.2016.02.012
- Malliarosa, F., & Vazirgiannis, M. (2013). Clustering and community detection in directed networks: A survey. Physics Reports, 533, 95–142. https://doi.org/10.1016/j.physrep.2013.08.002
- Owrang, O. M. (2000). Database systems techniques and tools in automatic knowledge acquisition for rule-based expert systems. In Knowledge-based systems (Vol. 1) (pp. 201–248). https://doi.org/10.1016/B978-012443875-0/50009-4
10.1016/B978-012443875-0/50009-4 Google Scholar
- Ozdena, A., Faghria, A., & Li, M. (2016). Using Knowledge-Automation Expert Systems to Enhance the Use and Understanding of Traffic Monitoring Data in State DOTs. International Conference on Sustainable Design, Engineering and Construction. Procedia Engineering (145, pp. 980-986). Arizona, United States. doi: https://doi.org/10.1016/j.proeng.2016.04.127.
- Rafea, A., Hassen, H., & Hazman, M. (2003). Automatic knowledge acquisition tool for irrigation and fertilization expert systems. Expert Systems with Applications, 24, 49–57. https://doi.org/10.1016/S0957-4174(02)00082-9
- Ruiz-Mezcua, B., Garsia-Crespo, A., Lopez-Cuadrado, J. L., & Gonzalez-Carrasco, I. (2011). An expert system development tool for non AI experts. Expert Systems with Applications, 38, 597–609. https://doi.org/10.1016/j.eswa.2010.07.009
- Swaffield, G., & Knight, B. (1990). Applying systems analysis techniques to knowledge engineering. Expert Systems, 7, 82–92.
10.1111/j.1468-0394.1990.tb00170.x Google Scholar
- Tselykh, A., & Tselykh, L. (2015). Methodology for comparative cognitive modeling based on the analysis of fuzzy target and control factors. Izvestiya SFedU. Engineering Sciences, 7(168), 101–115.
- Tselykh, A., Vasilev, V., Tselykh, L., & Barkovskii, S. (2017). Method maximizing the spread of influence in directed signed weighted graphs. Advances in Electrical and Electronic Engineering, (2), 203–214. https://doi.org/10.15598/aeee.v15i2.1950
- Zarikas, V., Papageorgiou, E., & Regner, P. (2015). Bayesian network construction using a fuzzy rule based approach for medical decision support. Expert Systems, 32, 344–369. https://doi.org/10.1111/exsy.12089
