Abstract
Knowledge representation plays a very important role for designing knowledge base systems as well as intelligent systems. Nowadays, there are many effective methods for representing such as: semantic network, rule-base systems, computational network. Computational Objects Knowledge Base (COKB) can be used to represent the total knowledge and design the knowledge base of systems. In fact, a popular form of knowledge domain is knowledge about operations and computational relations, especially computational knowledge domain, such as: Linear Algebra, Analytic Geometry. However, COKB model and the other models have not solved yet some problems about operators: specification of operator, properties of operator, reducing an expression. In this paper, we will present a reducing model of COKB. This model, called Ops-model, represents knowledge about operators between objects and solve some problems related to these operators. Through that, the algorithms for designing inference engine of model have been built up. Moreover, Ops-model has been applied to specify a part of knowledge domain about Direct Current (DC) Electrical Circuits and construct a program for solving some problems on this knowledge domain.
This research is funded by Vietnam National University HoChiMinh City (VNU-HCM) under grant number C2014-26-02.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
van Harmelem, F., Lifschitz, V., Porter, B.: Bruce: Handbook of Knowledge Representation. Elsevier (2008)
Russell, S., Norvig, P.: Artificial Intelligence – A modern approach, 3rd edn. Prentice Hall (2010)
Sowa, J.F.: Knowledge Representation: Logical, Philosophical and Computational Foundations. Brooks/Cole (2000)
Helbig, H.: Knowledge Representation and the Semantics of Natural Language. Springer, Berlin (2006)
Tian, Y., Wang, Y., Hu, K.: A Knowledge Representation Tool for Autonomous Machine Learning Based on Concept Algebra. In: Gavrilova, M.L., Tan, C.J.K., Wang, Y., Chan, K.C.C. (eds.) Transactions on Computational Science V. LNCS, vol. 5540, pp. 143–160. Springer, Heidelberg (2009)
Wang, Y.: On Concept Algebra and Knowledge Representation. In: Proceeding of 5th IEEE International Conference on Cognitive Informatics (ICCI) (2006)
Do, N.: Intelligent Problem Solvers in Education: Design Method and Applications. In: Koleshko, V.M. (ed.) Intelligent Systems. InTech (2012)
Do, N., Nguyen, H.: A Reasoning method on Knowledge Base of Computational Ojects and Designing a System for automatically solving plane geometry problems. In: Proceeding of World Congress on Engineering and Computer Science 2011 (WCECS 2011), San Francisco, USA, pp. 294–299 (October 2011) ISBN: 978-988-18210-9-6
Yang, C., Cai, W.: Knowledge Representations based on Extension Rules. In: Proceedings of the 7th World Congress on Intelligent Control and Automation, Chongqing, China (2008)
Do, N.V., Nguyen, H.D., Mai, T.T.: Designing an Intelligent Problems Solving System Based on Knowledge about Sample Problems. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013, Part I. LNCS, vol. 7802, pp. 465–475. Springer, Heidelberg (2013)
Vietnam Ministry of Education and Training, Textbook and workbook of Physics. Publisher of Education (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Do, V.N., Nguyen, H.D. (2015). Reducing Model of COKB about Operators Knowledge and Solving Problems about Operators. In: Camacho, D., Kim, SW., Trawiński, B. (eds) New Trends in Computational Collective Intelligence. Studies in Computational Intelligence, vol 572. Springer, Cham. https://doi.org/10.1007/978-3-319-10774-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-10774-5_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10773-8
Online ISBN: 978-3-319-10774-5
eBook Packages: EngineeringEngineering (R0)