KNOWLEDGE REPRESENTATION AND INTELLIGENT SYSTEMS
Main Article Content
Abstract
Knowledge base is an important component of an intelligent system. For organizing the knowledge base, methods of knowledge representation need to be researched. Many methodologies for knowledge representation with strong and weak points exist. In addition, the inference engine is also an integral part of the knowledge base, which is the component that enables the system to operate, reason, and solve problems in the knowledge domain. This paper presents some common knowledge representation methods in intelligent systems, and the criteria of practical applications for evaluating those methods are also studied. The article will also introduce some intelligent, practical applications which require a complete organization of the knowledge base.
Keywords
automatic reasoning, intelligent system, knowledge engineering, knowledge representation, knowledge-based systems
Article Details
References
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Nguyen, H. D., Sakama, C., Sato, T., & Inoue, K. (2021). An efficient reasoning method on logic programming using partial evaluation in vector spaces. Journal of Logic and Computation, 31(5), 1298-1316.
Nguyen, H., Nguyen, L., Tran, N., Nguyen-Le, V. T., & Vu, S. (2021). Design an Intelligent Problem Solver in Mathematics based on Integrated-Knowledge Model. Proceedings of 13th IEEE International Conference on Knowledge and Systems Engineering (KSE 2021), Bangkok, Thailand, Nov. 2021
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Nguyen, H. Tran, T.V., Pham, X. T., Pham, V., & Nguyen, D. (2022b). Design Intelligent Educational Chatbot for Information Retrieval based on Integrated Knowledge Bases. IAENG International Journal of Computer Science, 49(2), 531-541.
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Phan, N. M., Nguyen, H., Le, T., Tran, A. D., & Tran, N. (2020). Design an Intelligent Problem Solver in Geometry based on Knowledge Model of Relations. Engineering Letters, 28(4), 1108-1117.
Plotkin, B., & Plotkin, T. (2005). An algebraic approach to knowledge base models informational equivalence. Acta Appl. Math, 89(1-3), 109-134.
Plotkin, T., & Knyazhansky, M. (2012). Symmetries of knowledge bases. Ann Math Artif Intell, 64, 369-383.
Riano, D. (2006). New Artificial Intelligence Systems in Geriatric Medicine. The 51st National Congress of the Italian Society of Gerontology and Geriatric 2006, Florence, Italy.
Russell, S., & Norvig, P. (2010). Artificial Intelligent – A modern approach (third edition). Prentice Hall.
Tian, Y, Wang, Y., & Hu, K. (2009). A Kmowledge Representation Tool for Autonomous Machine Learning Based on Concept Algebra. Lecture Notes of Computer Science, 5540, 143-160, Springer.
Tomczak, J., & Swiatek, J. (2009). Knowledge acqusition method for Realational Knowledge Representation. Proceeding of 7th Workshop on Advanced Control and Diagnosis (ACD 2009), Poland, Nov. 2009.
Tran, M. C., Bui, D. T., Truong, T. T. H., & Nguyen, X. H. (2011). Phan hoach he suy dan [The partition of an inference system]. Proceedings of the national conference on Information and Communication Technology (VNICT 2011), (pp. 418-426), Can Tho.
Truong, D., Nguyen, H. D., Vu, S., Pham, V., & Nguyen, D. (2022). Construct an Intelligent Querying System in Education based on Ontology Integration. The 2nd IEEE International Conference on Computing 2022 (ICOCO 2022), Kota Kinabalu, Malaysia, Nov. 2022.
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Vossen, P., & Fellbaum, C. (2022). The global WordNet association. http://www.globalwordnet.org/ (Access on 09 Oct. 2022)
Wang, Y, Raskin, V., Rayz, J., Baciu, G., Ayesh, A.,… Howard, N. (2020). Cognitive Computing: Methodologies for Neural Computing and Semantic Computing in Brain-Inspired Systems. In Cognitive Analytics, IGI Global.
Worlfarm Alpha (2022). Retrieved from https://www.wolframalpha.com/examples/ (Access on 09 Oct. 2022)
Yun, W., Zhang, X., Li, Z., Liu, H., & Han, M. (2021). Knowledge modeling: A survey of processes and techniques. International Journal of Intelligent Systems, 36(4), 1686-1720.
Yang, C., & Cai, W. (2008). Knowledge Representations based on Extension Rules. Proc. the 7th World Congress on Intelligent Control and Automation, Chongqing, China.
Chakraborty, N., Lukovnikov, D., Maheshwari, G., Trivedim P., & Lehmann, J. (2021). Introduction to neural network‐based question answering over knowledge graphs. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(3), e1389.
Chen, X., Jia, S., & Xiang, Y. (2020). A review: Knowledge reasoning over knowledge graph. Expert Systems with Applications, 141, 112948.
Cīrulis, J. (1999). An Algebraic Approach to Knowledge Representation. Mathematical Foundations of Computer Science 1999, Lecture Notes in Computer Science, 1672, 1999, 299-309.
Davis, R., Shrobe, H., & Szolovits, P. (1993). What is a Knowledge Representation? AI Magazine, 14(1), 17-33.
Dovier, A., Formisano, A., Gupta, G., Hermenegildo, M., Pontelli, E., & Rocha, R. (2022). Parallel logic programming: A sequel. Theory and Practice of Logic Programming, 22(6), 906-973.
Do, N. (2009). Computational Networks for Knowledge Representation. Proc. of ICCSISE 2009, V, Singapore.
Do, N. (2012). Intelligent Problem Solvers in Education: Design Method and Applications. In Intelligent Systems, Koleshko, V.M. (Ed.). InTech.
Do, N. V. (2014). Ontology COKB for designing knowledge-based systems. Proceeding of 13th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SOMET 2014), (pp. 354-373), IOS press.
Do, N., Nguyen, H. D., & Selamat, A. (2018). Knowledge-based model of Expert systems using Rela-model. International Journal of Software Engineering and Knowledge Engineering, 28(8), 1047-1090.
Do, V. N., Nguyen, D. H., & Nguyen, T. N. D. (2022). Giao trinh Cac he co so tri thuc (tai ban)[Knowledge-based Systems (second edition)]. VNU-HCM.
EURECA (2022). Retrieved from http://eurecaproject.eu/about (Access on 09 Oct. 2022).
Gruber, T. (2009). Ontology. Entry in Ecyclopedia of Database Systems, Liu, L., & Ozsu, M. (Eds), (pp. 1963-1965), Springer-Verlag.
Helbig, H. (2005). Knowledge representation with multilayered extended semantic net-works: the MultiNet paradigm. Retrieved from http://pi7.fernuni-hagen.de/forschung/multinet/multinet_en.html
Hopgood, A. (2021). Intelligent Systems for Engineers and Scientists: A Practical Guide to Artificial Intelligence. CRC press.
Huynh, T., Nguyen, H., Zelinka, I., Pham, X.H., Pham, V., Selamat, A., & Krejcar, O. (2022). A method to detect influencers in social networks based on the combination of amplification factors and content creation. PLOS ONE, 17(10), e0274596.
Jiang, J., & Zhang, J. (2012). A review and prospect of readable
machine proofs for Geometry theorems. J Syst Sci Complex, 25, 802-820.
Knyazhansky, M. (2012). Knowledge Bases over algebraic models: Some notes about information equivalence. Intenational Journal of Knowledge Management, 8(1).
Lehmann, F. (2008). Semantic Networks in Artificial Intelligence. Elsevier Science Ltd.
Le, T. H., Tu, M. P., & Huynh, Q. T. (2006). Tac tu – Cong nghe phan mem huong tac tu [Agents - Software Engineering with Agents]. Hanoi Publisher of Science and Technology.
Le, T., Luu, S., Nguyen, H., & Do, N. (2019). Knowledge representation method for designing an Intelligent Tutoring System in Learning of courses about Algorithms. Proceedings of 2019 25th Asia-Pacific Conference on Communications (APCC 2019), 310-315.
Marakakis, E., Kondylakis, H., & Papadaki, N. (2012). Knowledge Representation in a Proof checker for Logic Programs. Advances in Knowledge Representation, 161-180.
Mou, X., Mao, L. X., Liu, H. C., & Zhou, M. (2022). Spherical linguistic Petri nets for knowledge representation and reasoning under large group environment. IEEE Transactions on Artificial Intelligence, 3(3), 402-413.
Noy, N., & McGuinness, D. (2013). Final Report on the 2013 NSF Workshop on Research Challenges and Opportunities in Knowledge Representation. NSF Workshop Report.
Nguyen, H. D., Do, N. V., Pham, V. T., & Inoue, K. (2018). Solving problems on a knowledge model of operators and application. Int. J. Digital Enterprise Technology (IJDET), 1(1/2), 37-59.
Nguyen, H. D., Do, N., Mai, T., & Pham, V. (2019). A method for designing the Intelligent system in learning of Algorithms. Frontiers in Artificial Intelligence and Applications, 318, 658-671.
Nguyen, H., Do, N., Tran, N., Pham, X. H., & Pham, V. (2020a). Some criteria of the Knowledge Representation method for an Intelligent Problem Solver in STEM education. Applied Computational Intelligence and Soft Computing, 2020.
Nguyen, H., Do, N. V., Pham, V. T., Selamat, A., & Herrera-Viedma, E. (2020b). A method for knowledge representation to design Intelligent Problems Solver in mathematics based on Rela-Ops model. IEEE Access, 8, 76991-77012.
Nguyen, H. D., Nguyen, K., Hoang, S., & Huynh, T. (2020c). Design a management system for the influencer marketing campaign on social network. Proceedings of 9th International Conference on Computational Data and Social Networks (CSoNet 2020), (pp. 139-151), Dallas, USA, Dec. 2020.
Nguyen, H. D., Sakama, C., Sato, T., & Inoue, K. (2021). An efficient reasoning method on logic programming using partial evaluation in vector spaces. Journal of Logic and Computation, 31(5), 1298-1316.
Nguyen, H., Nguyen, L., Tran, N., Nguyen-Le, V. T., & Vu, S. (2021). Design an Intelligent Problem Solver in Mathematics based on Integrated-Knowledge Model. Proceedings of 13th IEEE International Conference on Knowledge and Systems Engineering (KSE 2021), Bangkok, Thailand, Nov. 2021
Nguyen, H., Do, N., & Pham, V. (2022a). A methodology for designing Knowledge-based Systems and Application. In: Applications Computational Intelligence Multi Disciplinary Research, A. Elgnar et al. (Eds.). Elsevier.
Nguyen, H. Tran, T.V., Pham, X. T., Pham, V., & Nguyen, D. (2022b). Design Intelligent Educational Chatbot for Information Retrieval based on Integrated Knowledge Bases. IAENG International Journal of Computer Science, 49(2), 531-541.
Pease, A., Li, J., & Nomorosa, K., (2012). WordNet and SUMO for Sentiment Analysis. Proceedings of the 6th International Global Wordnet Conference (GWC2012), Matsue, Japan.
Phan, N. M., Nguyen, H., Le, T., Tran, A. D., & Tran, N. (2020). Design an Intelligent Problem Solver in Geometry based on Knowledge Model of Relations. Engineering Letters, 28(4), 1108-1117.
Plotkin, B., & Plotkin, T. (2005). An algebraic approach to knowledge base models informational equivalence. Acta Appl. Math, 89(1-3), 109-134.
Plotkin, T., & Knyazhansky, M. (2012). Symmetries of knowledge bases. Ann Math Artif Intell, 64, 369-383.
Riano, D. (2006). New Artificial Intelligence Systems in Geriatric Medicine. The 51st National Congress of the Italian Society of Gerontology and Geriatric 2006, Florence, Italy.
Russell, S., & Norvig, P. (2010). Artificial Intelligent – A modern approach (third edition). Prentice Hall.
Tian, Y, Wang, Y., & Hu, K. (2009). A Kmowledge Representation Tool for Autonomous Machine Learning Based on Concept Algebra. Lecture Notes of Computer Science, 5540, 143-160, Springer.
Tomczak, J., & Swiatek, J. (2009). Knowledge acqusition method for Realational Knowledge Representation. Proceeding of 7th Workshop on Advanced Control and Diagnosis (ACD 2009), Poland, Nov. 2009.
Tran, M. C., Bui, D. T., Truong, T. T. H., & Nguyen, X. H. (2011). Phan hoach he suy dan [The partition of an inference system]. Proceedings of the national conference on Information and Communication Technology (VNICT 2011), (pp. 418-426), Can Tho.
Truong, D., Nguyen, H. D., Vu, S., Pham, V., & Nguyen, D. (2022). Construct an Intelligent Querying System in Education based on Ontology Integration. The 2nd IEEE International Conference on Computing 2022 (ICOCO 2022), Kota Kinabalu, Malaysia, Nov. 2022.
Van Harmelen, F., Lifschitz, V., & Porter, B. (Eds.). (2008). Handbook of knowledge representation. Elsevier.
Vossen, P., & Fellbaum, C. (2022). The global WordNet association. http://www.globalwordnet.org/ (Access on 09 Oct. 2022)
Wang, Y, Raskin, V., Rayz, J., Baciu, G., Ayesh, A.,… Howard, N. (2020). Cognitive Computing: Methodologies for Neural Computing and Semantic Computing in Brain-Inspired Systems. In Cognitive Analytics, IGI Global.
Worlfarm Alpha (2022). Retrieved from https://www.wolframalpha.com/examples/ (Access on 09 Oct. 2022)
Yun, W., Zhang, X., Li, Z., Liu, H., & Han, M. (2021). Knowledge modeling: A survey of processes and techniques. International Journal of Intelligent Systems, 36(4), 1686-1720.
Yang, C., & Cai, W. (2008). Knowledge Representations based on Extension Rules. Proc. the 7th World Congress on Intelligent Control and Automation, Chongqing, China.