A comparative study on the performance of rule engines in automated ontology learning: a case study with erythemato-squamous disease (ESD). Issue 4 (7th April 2020)

• Record Type: Journal Article
• Authors: S, Sivasankari; Punnoose, Dinah; D, Krishnamoorthy
• Is Part Of: International journal of intelligent unmanned systems. Volume 8:Issue 4(2020)
• Page Start: 267
• Page End: 280
• Publication Date: 2020-04-07
• Subjects: Ontology -- Rule engine -- OWL knowledge -- Jess -- Drools -- Semantic Web Rule Language; Vehicles, Remotely piloted -- Periodicals; Robots -- Control systems -- Periodicals; Mechanical engineering -- Robots -- Periodicals; Robotics -- Periodicals; Submersibles -- Periodicals; Space vehicles -- Command control systems -- Periodicals; 629.046
• DOI: 10.1108/IJIUS-08-2019-0047
• Languages: English
• ISSNs: 2049-6427

Abstract

Abstract : Purpose: Erythemato-squamous disease (ESD) is one of the complex diseases related to the dermatology field. Due to common morphological features, the diagnosis of ESDs become stringent and leads to inconsistency. Besides, diagnosis has been done on the basis of inculcated visible symptoms pertinent with the expertise of the physician. Hence, ontology construction for ESD is essential to ensure credibility, consistency, to resolve lack of time, labor and competence and to diminish human error. Design/methodology/approach: This paper presents the design of an automatic ontology framework through data mining techniques and subsequently depicts the diagnosis of ESD using the available knowledge- and rule-based system. Findings: The rule language (Semantic Web Rule Language) and rule engine (Jess and Drools) have been integrated to explore the severity of the ESD and foresee the most appropriate class to be suggested. Social implications: In this paper, the authors identify the efficiency of the rule engine and investigate the performance of the computational techniques in predicting ESD using three different measures. Originality/value: Primarily, the approach assesses transfer time for total number of axioms exported to rule engine (Jess and Drools) while the other approach measures the number of inferred axioms (process time) using the rule engine while the third measure calculates the time to translate the inferred axioms to OWL knowledge (execution time).