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Ontology Engineering: The Brain Gene Ontology Case Study

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Thesis(1.78 MB)

Date

2008

Authors

Supervisor

Jain, Vishal
Gottgtroy, Paulo

Item type

Thesis

Degree name

Master of Computer and Information Sciences

Journal Title

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Publisher

Auckland University of Technology

Abstract

The emergence of ontologies has marked another stage in the evolution of knowledge engineering. In the biomedical domain especially, a notable number of ontologies have been developed for knowledge acquisition, maintenance, sharing and reuse from large and distributed databases in order to reach the critical requirements of biomedical analysis and application. This research aims at the development of a Brain Gene Ontology by adopting a constructive IS methodology which tightly combines the processes of ontology learning, building, reuse and evaluation together. Brain Gene Ontology is a part of the BGO project that is being developed by KEDRI (Gottgtroy and Jain, 2005). The objective is to represent knowledge of the genes and proteins that are related to specific brain disorders like epilepsy and schizophrenia. The current stage focuses on the crucial neuronal parameters such as AMPA, GABA, CLC and SCN through their direct or indirect interactions with other genes and proteins. In this case, ontological representations were able to provide the conceptual framework and the knowledge itself to understand more about relationships among those genes and their links to brain disorders. It also provided a semantic repository of systematically ordered molecules concerned. The research adopts Protégé-Frames, which is an open source ontology tool suite for BGO development. Some Protégé plug-ins were also used to extend the applicable functions and improve knowledge representation. Basically, the research discusses the availability and the framework of the constructive Information System research methodology for ontology development, it also describes the process that bridges different notions of the brain, genes and proteins in various databases, and illustrates how to build and implement the ontology with Protégé-Frames and its plug-ins. The results of the BGO development proved that the constructive IS methodology does help to fill in the cognitive gap between domain users and ontology developers, the extensible, component-based architectures of Protégé-Frames significantly support the various activities in the ontology development process, and through explicitly specifying the meaning of fundamental concepts and their relations, ontology can actually integrate knowledge from multiple biological knowledge bases.

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Keywords

Biological data, Constructive approach, Knowledge modelling, Bioinformedical database

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Permanent link

https://hdl.handle.net/10292/104

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