Research Article
Design of Knowledgebase for EES using Object - Relational Data Modeling
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 184 - Issue 21 |
| Published: Jul 2022 |
| Authors: Justus Selwyn |
10.5120/ijca2022922234
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Justus Selwyn . Design of Knowledgebase for EES using Object - Relational Data Modeling. International Journal of Computer Applications. 184, 21 (Jul 2022), 8-14. DOI=10.5120/ijca2022922234
@article{ 10.5120/ijca2022922234,
author = { Justus Selwyn },
title = { Design of Knowledgebase for EES using Object - Relational Data Modeling },
journal = { International Journal of Computer Applications },
year = { 2022 },
volume = { 184 },
number = { 21 },
pages = { 8-14 },
doi = { 10.5120/ijca2022922234 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2022
%A Justus Selwyn
%T Design of Knowledgebase for EES using Object - Relational Data Modeling%T
%J International Journal of Computer Applications
%V 184
%N 21
%P 8-14
%R 10.5120/ijca2022922234
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This work is focused on designing a knowledgebase for an Expert Education System (EES) that will help the learners to know where, what, how, and when is the required knowledge available in the knowledgebase. A three-layered architecture of a Knowledgebase is proposed, and have given some basic definitions for the concepts, knowledge units, and persistent objects. Definitions are given in FOL, frames, DL, and OR data models. Integrating these into a KB will serve as a backend for the EES.
References
- Philippe Martin, "Knowledge representation in RDF/XML, KIF, Frame-CG and Formalized-English", Distributed System Technology Centre, QLD, Australia, July 15–19, 2002
- Hien D. Nguyen ,1,2Nhon V. Do, Et. Al., Some Criteria of the Knowledge Representation Method for an Intelligent Problem Solver in STEM Education, Applied Computational Intelligence and Soft Computing, Vol. 2020, No. 1, pp. 1-14, May 2020
- Randall Davis, "What Is a Knowledge Representation?", Association for the Advancement of Artificial Intelligence, Vol. 14, No. 1, 1993.
- Lin, J., Zhao, Y., Huang, W. et al. Domain knowledge graph-based research progress of knowledge representation. Neural Comput&Applic 33, 681–690 (2021).
- Ronald J. Brachman, Hector J. Levesque, Knowledge Representation and Reasoning, Morgan Kaufmann, 2004
- Chein, M., Mugnier, M.-L.,Graph-based Knowledge Representation: Computational Foundations of Conceptual Graphs, Springer, 2009.
- Y. Wang and M. Valipour, “Formal ontology generation by deep machine learning,” in Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics &CognitiveComputing (ICCI∗CC), Oxford, UK, July 2017.
- Hermann Helbig: Knowledge Representation and the Semantics of Natural Language, Springer, Berlin, Heidelberg, New York 2006
- N. V. Do, H. D. Nguyen, and A. Selamat, “Knowledge-based model of expert systems using rela-model,” International Journal of Software Engineering and Knowledge Engineering, vol. 28, no. 8, pp. 1047–1090, 2018.
- John F. Sowa: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks/Cole: New York, 2000
- Amjad Farooq and Imran Mir, “Design and Development of a University Web Tools”, International journal of multidisciplinary sciences and engineering, vol. 1, no. 2, 2010.
- Christy Dharmasingh, Justus S., “A Prediction Model addressing the Changes and Challenges in Adopting SEaaS Model during COVID-19”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 9, Issue 4, pp. 14-20, Aug 2021.
- Hepsiba Mabel., Justus S., “Performance Assessment of 3-Layered Knowledgebase and Representation Models: An Empirical Study”, International Journal of Knowledge Engineering and Data Mining, Vol. 3, No. 4, pp. 239-254, Dec 2015
- Gartland-Jones. A., and P. Copley, “The Suitability of Genetic Algorithms for Musical Composition”, Contemporary Music Review, Vol. 22, No. 3, pp. 43–55, 2003.
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