Ontology is what makes data interesting: Interestingness framework for COVID-19 corpora

The COVID-19 pandemic has already shown to be a worldwide threat, demonstrating how susceptible humans may be. It has also inspired experts from a range of aspects and countries to find the potential solution to control the widespread. In line with this, our research proposes a novel framework for finding interesting facts from COVID-19 corpora using domain ontology. Since data mining with domain knowledge provides semantically rich facts, we use ontology in our proposed approaches. Most of the state-of-the-art methods rely on instance level or user intervention. These methods do not entirely exploit the richness of ontology. In this work, we demonstrate how to extract exciting rules from data at ontology's schema and instance levels. Our experiments were carried out on two COVID-19 corpora that depict COVID-19 patients' symptoms and drug information. The proposed framework outperformed the traditional methods by reducing the number of rules by 70% and generating semantic-rich rules that are more user-readable and quickly adopted by decision-makers. Furthermore, to support our claims, we compared the outcomes of the proposed framework with the most recent approach in the field. Also, statistically significant tests and domain expert evaluations are conducted to validate our framework.