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Abstract
The COVID-19 pandemic has generated massive data in the healthcare sector in recent years, encouraging researchers and scientists to uncover the underlying facts. Mining interesting patterns in the large COVID-19 corpora is very important and useful for the decision makers. This paper presents a novel approach for uncovering interesting insights in large datasets using ontologies and BERT models. The research proposes a framework for extracting semantically rich facts from data by incorporating domain knowledge into the data mining process through the use of ontologies. An improved Apriori algorithm is employed for mining semantic association rules, while the interestingness of the rules is evaluated using BERT models for semantic richness. The results of the proposed framework are compared with state-of-the-art methods and evaluated using a combination of domain expert evaluation and statistical significance testing. The study offers a promising solution for finding meaningful relationships and facts in large datasets, particularly in the healthcare sector.
Acknowledgments
We also extend our special thanks to the e-Health section of HFWS, Government of Karnataka, for providing all the necessary support and encouragement. We extended our gratitude to JSS Mahavidyapeeta, Mysore and my parent institute, JSSATE Bangalore, for allowing me to pursue my doctoral degree. Also, we would like to thank five anonymous reviewers for commenting on earlier versions of this paper.
Disclosure statement
No potential conflict of interest was reported by the author(s).