Development of the InBan_CIDO Ontology by Reusing the Concepts along
  with Detecting Overlapping Information
  The covid19 pandemic is a global emergency that badly impacted the economies
of various countries. Covid19 hit India when the growth rate of the country was
at the lowest in the last 10 years. To semantically analyze the impact of this
pandemic on the economy, it is curial to have an ontology. CIDO ontology is a
well standardized ontology that is specially designed to assess the impact of
coronavirus disease and utilize its results for future decision forecasting for
the government, industry experts, and professionals in the field of various
domains like research, medical advancement, technical innovative adoptions, and
so on. However, this ontology does not analyze the impact of the Covid19
pandemic on the Indian banking sector. On the other side, Covid19IBO ontology
has been developed to analyze the impact of the Covid19 pandemic on the Indian
banking sector but this ontology does not reflect complete information of
Covid19 data. Resultantly, users cannot get all the relevant information about
Covid19 and its impact on the Indian economy. This article aims to extend the
CIDO ontology to show the impact of Covid19 on the Indian economy sector by
reusing the concepts from other data sources. We also provide a simplified
schema matching approach that detects the overlapping information among the
ontologies. The experimental analysis proves that the proposed approach has
reasonable results.
