Hybrid Technique for Effective Knowledge Representation in Normal Life
Poonam Tanwar1, T. V. Prasad2, Kamlesh Dutta3
1Dr. POONAM TANWAR, Associate Prof, Dept. of CSE, Manav Rachna International University, Faridabad, Haryana, India.
2DR. T. V. PRASAD, Dean of Computing Sciences, Visvodaya Technical Academy, Kavali, Andhra Pradesh, India
3DR. KAMLESH DUTTA, Associate Prof & HOD (CSE), National Institute of Technology Hamirpur, Himachal, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 991-898 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8218088619/2019©BEIESP | DOI: 10.35940/ijeat.F8218.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Knowledge is essential for daily life. People can gather knowledge from their routine work like reading newspaper, watching T.V, discussions, experience and many more are the source of knowledge. An artificial intelligent (AI) system must be able to do the same. So there is a requirement of effective knowledge representation (KR) technique that is able to represent the knowledge from above defined sources and works for daily life. An inference mechanism is also required for reasoning the knowledge from artificial intelligent system. The purpose of this paper is to present the implemented conceptual view of hybrid KR technique that works for representing the knowledge required for daily life.
Keywords: Knowledge Representation (KR), Semantic Net, Script, Supervised Learning, Forward Chaining.