Experimental Performance Evaluation of Uncertain Ontology Representation Using Bayesian Model

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Experimental Performance Evaluation of Uncertain Ontology Representation Using Bayesian Model 9 Pages Posted: 5 Dec 2022 See all articles by Sanjay Kumar AnandSanjay Kumar AnandGuru Gobind Singh Indraprastha UniversitySuresh KumarNetaji Subhas University of Technology, East Campus Date Written: July 31, 2022 Abstract Uncertain knowledge allows us to learn new knowledge and leads us to develop a new domain model. The medical domain is a very crucial area of research where a wrong decision taken by a physician can create a significant health issue. Based on patient sensations and certain conventional measurements, physicians attempt to identify possible diseases and doubts. But they fail to do so and deal with uncertainty. Therefore, we need to be certain of the method of any new observation. The Bayesian network (BN) model deals with uncertainty for better knowledge modeling and representation, respectively. This paper focuses on knowledge representation and the prediction of uncertain knowledge in domain ontology (Liver disorder) using the BN model. In this paper, we have evaluated the accuracy of each node and experimentally validated it. We have also compared our proposed model (BN Model) with Random Forest (RF), Hidden markov model (HMM), Support vector machine (SVM), K-NN, and Neural Network (NN) algorithms to check model efficiency to find its sensitivity, specificity, and accuracy. From the result, we found that our approach (BN Model) on the same dataset (Liver Disorder Ontology) outperforms in comparison to other modeling techniques. Keywords: Decision rules, Information retrieval, Knowledge representation, Ontology, uncertainty JEL Classification: C02 Suggested Citation: Suggested Citation Anand, Sanjay Kumar and Kumar, Suresh, Experimental Performance Evaluation of Uncertain Ontology Representation Using Bayesian Model (July 31, 2022). Available at SSRN: https://ssrn.com/abstract=4292348 Sanjay Kumar Anand (Contact Author) Guru Gobind Singh Indraprastha University ( email ) Suresh Kumar Netaji Subhas University of Technology, East Campus ( email ) Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 6 Abstract Views 13 PlumX Metrics Related eJournals 4th International Conference on Communication & Information Processing (ICCIP) 2022 Follow 4th International Conference on Communication & Information Processing (ICCIP) 2022 Subscribe to this free journal for more curated articles on this topic FOLLOWERS 11 PAPERS 0 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo-4dc in 0.187 seconds