A Novel Semi-Automated Ontology Construction
Framework (SOCF) for Psoriasis Detection: Pioneering
the Psoriasis Risk Assessment Remedy (PRAR) Database
SIVASANKARI. S.1, Gracia Jacob SHOMONA2
1 Anna University, SSN College of Engineering, Department of CSE,
Kalavakkam, Chennai-603110, India
2 SSN College of Engineering, Department of CSE, Kalavakkam,
Abstract: Ontology provides an organizational framework of concepts and a system that depicts hierarchical and associative relationships pertaining to an application domain. The possibility of reuse and data sharing permitted by ontology, along with the formal structure coupled with hierarchies of concepts and their inter-relationships offer the opportunity to draw complex inferences and reasoning. This rationale was the motivation to construct an ontology for Psoriasis Risk Assessment and Remedy (PRAR). This paper targets two issues: (i) Need for a medical database to derive Ontology (ii) Methodology for design of Semi-Automated Ontology Construction framework (SOCF) from pioneered data. Psoriasis is one of the most recurrent skin issues in India and the world at large and hence this paper targeted the need to generate a Psoriasis Remedy Database and automatically infer the relations between the Symptoms, Causes and Treatment through Semi-Automated Ontology Reasoning and Inference. The proposed system incorporated two phases: Formulation of a novel database for Psoriasis Risk Assessment Remedy (PRAR) (ii) Articulation of a novel framework for Psoriasis detection through computational modeling and Ontology Construction. The proposed methodology was tested on 112 samples from the authenticated UCI Machine Learning Repository. The ontology developed using the proposed SOCF mapped the risk factors and remedies for Psoriasis detection with 98.7% accuracy, this being reported for the first time.
Keywords: Psoriasis, OWL, XML, Mapping, Ontology, Reasoning, Inference.
CITE THIS PAPER AS:
SIVASANKARI. S., Gracia Jacob SHOMONA, A Novel Semi-Automated Ontology Construction Framework (SOCF) for Psoriasis Detection: Pioneering the Psoriasis Risk Assessment Remedy (PRAR) Database, Studies in Informatics and Control, ISSN 1220-1766, vol. 25(2), pp. 237-244, 2016.
The field of medicine encounters the emergence of new diseases every day. The diagnosis of these increasing numbers of diseases is becoming difficult. The data collected during the diagnosis and treatment of these diseases is vast, unorganized and hence difficult to manage, thus raising the burden of medical practitioners to monitor and maintain all the information across diverse diseases. This revealed the critical need for an information system that could provide the required solution for the existing scenario by structuring and organizing the existing data. The concept of ontology is believed to address this issue. Ontology is defined as a formal explicit representation of conceptualization of a domain that provides a platform for the sharing and reuse of knowledge across heterogeneous platforms (Jian-xu chen et al .2010). It contains semantic descriptions using concepts and relationship abstractions in a way that is readable by both humans and machine (Hadzic & Chang 2005). An ontology-based approach represents entities, ideas and events, along with their properties and relations, as a form of knowledge representation. In the medical world, ontologies represent diseases, its associated symptoms, tests and treatment along with their properties and relationships. Ontological framework can be constructed for diseases of varied nature provided there is availability of data to support the framework. The main objectives of this paper is (i) to formulate a novel database for Psoriasis detection and report (PRAR), its associated symptoms and diseases.(ii) Propose a Semi-automated Ontology Construction Framework (SOCF) that can be applied to the published database (PRAR).
Psoriasis is a recurrent dermatology ailment that is more often revealed as systemic manifestations, especially arthritis manifestations (Gudjonsson 2007). An approximate 2 percent of adults suffer from this ailment, and it appears to be prevalent among men and women irrespective of their age. However, Psoriasis onset is most likely to infect people in the age groups ranging from 15 to 30 years (Gudjonsson 2007).The clinical course is capricious (Langley et al 2005)]. Personalized and tailored therapy can aid in reducing the associated melancholy and enhance the patient’s quality of life. Statistics suggest that close to one-third of patients with Psoriasis have a first-degree relative with the condition. Research suggests inheritance of the ailment from multiple factors (Langley et al 2005, Capon et. al. 2004).
With the unremitting growth of scientific information sources, the need for integrated access to these sources becomes ever more imperative. The aim of the Psoriasis Risk Assessment Remedy (PRAR) database is to allow access to multiple information sources in the medical domain through web interface. The prevalence, lack of appropriate data on Psoriasis, their risk factors and remedies was the rationale for this research. Hence the authors attempted to derive a novel Psoriasis Risk Assessment Remedy (PRAR) database and propose computational methods to enable semi-automated construction of ontology to map the derived data to Psoriasis detection.
Section 2 narrates the background work. Section 3 presents the materials used and the method for implementing the framework. Section 4 highlights the proposed ontology framework construction for Psoriasis risk Assessment Remedy (PRAR) database. Section 5 concludes the paper and also identifies avenues to further research in this area.
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