Modeling a Classification Scheme of Epileptic Seizures Using Ontology Web Language

Modeling a Classification Scheme of Epileptic Seizures Using Ontology Web Language

Bhaswati Ghosh (Cleveland State University, USA), Partha S. Ghosh (Cleveland Clinic Foundation, USA) and Iftikhar U. Sikder (Cleveland State University, USA)
DOI: 10.4018/jcmam.2010072004
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Abstract

Ontology-based disease classification offers a way to rigorously assign disease types and to reuse diagnostic knowledge. However, ontology itself is not sufficient for fully representing the complex knowledge needed in classification schemes which are continuously evolving. This article describes the application of SWRL/OWL-DL to the representation of knowledge intended for proper classification of a complex neurological condition, namely epilepsy. The authors present a rigorous and expandable approach to the ontological classification of epileptic seizures based on the 1981ILAE classification. It provides a classification knowledge base that can be extended with rules that describe constraints in SWRL. Moreover, by transforming an OWL classification scheme into JESS (rule engine in Java platform) facts and by transforming SWRL constraints into JESS, logical inferences and reasoning provide a mechanism to discover new knowledge and facts. The logic representation of epileptic classification amounts to greater community understanding among practitioners, knowledge reuse and interoperability.
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Ontology For Crafting Specifications Of Domain Concepts

Historically, expert systems have been used to assist in medical decision making involving diagnosis, prediction, evaluation, monitoring (Heathfield, 1999; Hernandez, Sancho, Belmonte, Sierra, & Sanz, 1994; Keles & Keles, 2008; Liebowitz, 1997; Tsumoto, 2003). By encapsulating domain knowledge into a set of rules, expert systems simulate the performance of one or more human experts with expert knowledge and experience in a specific problem domain. With the advent of Semantic Web movement, a growing interest in ontologies is being noticed as means of representing human knowledge and as critical components in knowledge management over the Web. Various research communities commonly assume that ontologies are the appropriate modeling structure for representing knowledge. While expert systems emphasize technology, ontologies emphasize knowledge. Ontologies make a domain specific knowledge base reusable, sharable and interoperable. Domain-specific questions can then be answered by reasoning over such highly specialized knowledge. Ontologies have evolved in computer science as computational artifacts to provide computer systems with a conceptual yet computational model of a particular domain of interest. While expert systems provide excellent tools for reasoning with domain rules, they often lack the means to resolve semantic ambiguities inherent in the predicates and related facts. Hence, a key requirement is to reason in a semantically consistent way is to exploit both the ontology and the rule-based knowledge to draw inferences.

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