Semantic association rules for data interestingness using domain ontology. (31st May 2023)

• Record Type: Journal Article
• Authors: Abhilash, C.B.; Mahesh, Kavi
• Is Part Of: International journal of metadata, semantics and ontologies. Volume 16:Number 1(2023)
• Page Start: 47
• Page End: 67
• Publication Date: 2023-05-31
• Subjects: semantic rules -- ontology-based techniques -- COVID-19 -- data interestingness -- association rule mining -- knowledge discovery; Metadata -- Periodicals; Semantic Web -- Periodicals; Ontologies (Information retrieval) -- Periodicals; Data structures (Computer science) -- Periodicals; Information theory -- Periodicals; 005.74
• Languages: English
• ISSNs: 1744-2621

Abstract

The COVID-19 pandemic is a major public health crisis threatening people's health, well-being, freedom to travel and the global economy. Understanding COVID-19 symptoms for determining the severity of cases is critical. This study aimed to discover interesting facts from the COVID-19 data set considering symptoms, medicines and comorbidity. For data mining research, the semantic web raises new possibilities. Resource Description Framework (RDF) triple format is commonly used to express semantic web data. Association Rule Mining (ARM) is one of the most effective methods of detecting frequent patterns. However, finding potential rules is a difficult task. We propose an improved method that uses ontology with ARM for finding semantic-rich rules from COVID-19 data sets. The outcomes are semantic association rules that are potentially beneficial for decision-makers. We compare our results with one of the most recent approaches in this field to demonstrate the importance of ontology-based methods.