In recent years, there has been significant growth in the use of ontology-based methods to
enhance data interestingness. These methods play a crucial role in knowledge …

In this paper, we present Knowledge4COVID-19, a framework that aims to showcase the
power of integrating disparate sources of knowledge to discover adverse drug effects …

RDF knowledge graphs (KG) are powerful data structures to represent factual statements
created from heterogeneous data sources. KG creation is laborious and demands data …

Ontology is a significant data model for identifying semantic information for Coronavirus
disease (COVID-19) discovery. Large-scale biological and general datasets have recently …

The article presents a workflow to create a question-answering system whose knowledge
base combines knowledge graphs and scientific publications on coronaviruses. It is based …

Abstract Information retrieval tasks have become an essential tool for biomedical research.
The growing tendency in the number of publications has made it necessary to develop and …

Over the past decade, the volume of data has experienced a significant increase, and this
growth is projected to accelerate in the coming years. Within the healthcare sector, various …

This paper describes an approach to mining interestingness in data by designing domain
ontology, COKPME and populating it with anonymized COVID-19 data from private hospital …

This paper presents the Coronavirus Disease Ontology (CovidO), a superset of the available
Coronavirus (COVID-19) ontologies, including all the possible dimensions. CovidO consists …

Finding interesting association rules is a popular and current topic in data mining. The
Apriori family of algorithms is built around two rule extraction measures: support and …