FaD-CODS Fake News Detection on COVID-19 Using Description Logics and Semantic Reasoning

Goel, K.; Gupta, C.; Rawal, R.; Agrawal, P.; Madaan, V.

COVID-19 has affected people in nearly 180 countries worldwide. This paper presents a novel and improved Semantic Web-based approach for implementing the disease pattern of COVID-19. Semantics gives meaning to words and defines the purpose of words in a sentence. Previous ontology approaches revolved around syntactic methods. In this paper, semantics gives due priority to understand the nature and meaning of the underlying text. The proposed approach, FaD-CODS, focuses on a specific application of fake news detection. The formal definition is given by depiction of knowledge patterns using semantic reasoning. The proposed approach based on fake news detection uses description logic for semantic reasoning. FaD-CODS will affect decision making in medicine and healthcare. Further, the state-of-the-art method performs best for semantic text incorporated in the model. FaD-CODS used a reasoning tool, RACER, to check the consistency of the collected study. Further, the reasoning tool performance is critically analyzed to determine the conflicts between a myth and fact.