Dynamic ontology based Q&A system for pandemic situations case study COVID-19 pandemics

Abstract

In dynamic pandemic situations like covid-19, Many writeups, reviews, articles have been published every day. Rapidly updated data leads information overload, which make the public difficult to keep up with the latest data on pandemic situation. This paper focuses on introduce an efficient Q&A system for dynamic pandemic situation which help public to update with the real time data. Several approaches including basic ontologies, expert knowledge base and linguistic knowledge have been used when model the knowledge base of Q&A systems. But these approaches are mainly based on experts’ knowledge and mainly human interaction in knowledge acquisition, less handling of multimodal data, inefficient inferencing. Even though there are number of solutions which help public to update with the pandemic data, there are no fully automated real time updated systems. So, the intention is to introduce a fully automated multimodal data based real time updated system. In order to archive this goal, fully automated dynamic ontology-based Q&A system was design, developed and evaluated for the pandemic situation like covid-19. Solution was design in such a way that users can enter question which is related to the covid-19 pandemic and retrieve a real time answer. Mainly the system is based on two modules as dynamic ontology module which use web scrapping for real time updated data extraction, process to map the changes in data and Q&A module which simplifies the questions into RDF triples based normal forms that effortlessly handled by database querying. Evaluation of the system was conducted two ways by evaluation of the dynamic ontology module and evaluation of the question and answer module. In both evaluation processes time evaluation and precision has considered.