Abstract
Background. Cognitive decision-making is a promising area of research. As the word, cognitive connote the human-like behaviour, and humans acquire their knowledge based on their day to day experience. To accomplish this proficiency our agent or system should behave astutely. Here the role of Knowledge representation comes into the picture, and only the best representation tool can fabricate the domain knowledge efficiently. In this paper, we present the ontological aspect to fabricate the domain knowledge for effective decision-making.
Objective. The objective of this paper is to exhibit the role of ontology for designing an effective knowledge representation system, which leads towards an effectual cognitive decision-making system.
Methodology. This paper selects a particular area “Electrical Motor” and builds up an ontology to demonstrate basic concepts related to the electric motor. This ontology is useful for answering the questions related to basic motor concepts; that is useful to educate the students.
Result. This paper deals with the realistic aspects related to the design of an ontology. It converses the anticipated experimental design issues and results.
Conclusion. Finally this paper concludes the ontology development process by describing the pros and cons. It also converses the future aspects (NLP Modelling) for knowledge representation, which can effectively model a domain knowledge.
A.P. Prajapati—is working in the area of “Cognitive Computing”.
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
References
Lee, K., Lee, J., Kwan, M.-P.: Location-based service using ontology-based semantic queries: a study with a focus on indoor activities in a university context. Comput. Environ. Urban Syst. 62, 41–52 (2017)
Liu, J., Liu, L., Xue, Y., Dong, J., Hu, Y., Hill, R., Guang, J., Li, C.: Grid workflow validation using ontology-based tacit knowledge: a case study for quantitative remote sensing applications. Comput. Geosci. 98, 46–54 (2017)
Raies, K., Khemaja, M., Mejbri, Y.: Automatic extraction of smart game based learning design expertise: an approach based on learning ontology. Innovations in Smart Learning. LNET, pp. 161–170. Springer, Singapore (2017). doi:10.1007/978-981-10-2419-1_23
Liu, J., Zheng, B.-J., Luo, L.-M., Zhou, J.-S., Zhang, Y., Yu, Z.-T.: Ontology representation and mapping of common fuzzy knowledge. Neurocomputing 215, 184–195 (2016)
Morente-Molinera, J.A., Wikström, R., Herrera-Viedma, E., Carlsson, C.: A linguistic mobile decision support system based on fuzzy ontology to facilitate knowledge mobilization. Dec. Support Syst. 81, 66–75 (2016)
Zhang, Y.-F., Tian, Y., Zhou, T.-S., Araki, K., Li, J.-S.: Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems. Comput. Methods Programs Biomed. 123, 94–108 (2016)
Chuprina, S., Alexandrov, V., Alexandrov, N.: Using ontology engineering methods to improve computer science and data science skills. Procedia Comput. Sci. 80, 1780–1790 (2016)
Maffei, A., Daghini, L., Archenti, A., Lohse, N.: CONALI ontology. A framework for design and evaluation of constructively aligned courses in higher education: putting in focus the educational goal verbs. Procedia CIRP 50, 765–772 (2016)
Chuprina, S., Postanogov, I., Nasraoui, O.: Ontology based data access methods to teach students to transform traditional information systems and simplify decision making process. Procedia Comput. Sci. 80, 1801–1811 (2016)
Wang, H., Wang, S.: Application of ontology modularization to human-web interface design for knowledge sharing. Expert Syst. Appl. 46, 122–128 (2016)
Tadjine, Z., Oubahssi, L., Piau-Toffolon, C., Iksal, S.: A process using ontology to automate the operationalization of pattern-based learning scenarios. In: Zvacek, S., Restivo, M.T., Uhomoibhi, J., Helfert, M. (eds.) CSEDU 2015. CCIS, vol. 583, pp. 444–461. Springer, Cham (2016). doi:10.1007/978-3-319-29585-5_26
Chun, S.A., Geller, J.: Developing a pedagogical cybersecurity ontology. In: Helfert, M., Holzinger, A., Belo, O., Francalanci, C. (eds.) DATA 2014. CCIS, vol. 178, pp. 117–135. Springer, Cham (2015). doi:10.1007/978-3-319-25936-9_8
Nuntawong, C., Namahoot, C.S., Brückner, M.: A semantic similarity assessment tool for computer science subjects using extended Wu & Palmer’s algorithm and ontology. In: Kim, K. (ed.) Information Science and Applications. LNEE, vol. 339. Springer, Heidelberg (2015). doi:10.1007/978-3-662-46578-3_118
Lalingkar, A., Ramnathan, C., Ramani, S.: Ontology-based smart learning environment for teaching word problems in mathematics. J. Comput. Educ. 1(4), 313–334 (2014). Springer, Heidelberg
Chi, Y.-L., Chen, T.-Y., Tsai, W.-T.: Creating individualized learning paths for self-regulated online learners: an ontology-driven approach. In: Rau, P.L.P. (ed.) CCD 2014. LNCS, vol. 8528, pp. 546–555. Springer, Cham (2014). doi:10.1007/978-3-319-07308-8_52
Altun, A., Kaya, G.: Development and evaluation of an ontology based navigation tool with learning objects for educational purposes. In: Huang, R., Kinshuk, Chen, N.-S. (eds.) The New Development of Technology Enhanced Learning: Concept, Research and Best Practices. Lecture Notes in Educational Technology, pp. 147–162. Springer, Heidelberg (2014)
Acknowledgments
I would like to thank Dr. P.S. Satsangi Sahab for his continuous inspirations and blessings and to Mr. Ashish Chandiok for his valuable guidance and support.
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
Prajapati, A.P., Chaturvedi, D.K. (2017). Ontology Based Knowledge Representation for Cognitive Decision Making in Teaching Electrical Motor Concepts. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Artificial Intelligence Trends in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-319-57261-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-57261-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57260-4
Online ISBN: 978-3-319-57261-1
eBook Packages: EngineeringEngineering (R0)