Candidate:
Mr. Gilbert Mayiga  

Faculty:
Computing and Information Technology

Thesis Title:

An Evaluation Framework for Large-Scale Ontology-based Biomedical Data Integrated Systems

Date:
Tuesday 10 November 2009

Time:
14:30 pm

Venue:
Faculty of Computing and IT Conference Room Block A

Abstract
There has been an emergence of various ontologies describing data from either the clinical or biological domains. Associated with this has been the development of biomedical ontologies using various strategies to integrate biological and clinical data across scope, process and differing levels of granularity. However, biomedical ontologies still find little use in distributed computing applications. This is largely attributed to: (i) lack of knowledge about user needs for biomedical data integration systems; and (ii) the absence of a general framework with tools and metrics to assess their relative suitability for specific applications. As a result, the reuse and wide
adoption of biomedical ontologies in distributed applications is not realized as yet. To bridge the gap this research develops a flexible framework for user evaluation of biomedical data integration ontologies. Requirements for the framework were tested in a descriptive survey using medical doctors and biologists as the study population. The research exploits concepts from systems theory, basic formal ontology, set theory and multi criteria evaluations to provide a unifying design of a flexible framework with a reference model and metrics for user evaluation of biomedical ontologies.

To test the utility of the framework an ontology evaluation tool was built as an application of the design. The tool was used to evaluate the infectious disease ontology and the results validated by a questionnaire based study. The results revealed a strong positive correlation (Pearson's r) between those where the tool was used and the corresponding ones from the questionnaire based study. Since the tool is an application of the evaluation framework design, the strong positive correlation provided empirical proof of the validity of the approach when scope, granular and process density were used as evaluation metrics. Using the evaluation framework with these
metrics therefore provides users with a valid approach for selecting an ontology suitable for a biomedical data integration task.

The framework contributes to the wide adoption and reuse of biomedical data integration ontologies in the following ways: (i) it creates improved understanding of user needs for biomedical ontology integration and evaluation; (ii) the tool can be used to gather requirements for extending existing ontologies, resulting into new ones that address current needs for biomedical data integration; (iii) a reference model and metrics for evaluating biomedical ontologies significantly contribute to integrating information systems and to scientific knowledge; (iv) The tool can also be used for training and extension of skills of clinicians and researchers in biomedicine. The novelty of this approach lies in the ability to; (i) bring together concepts from systems theory, basic formal ontology, set theory and multi criteria evaluations; and (ii) relate ontology structure to user requirements, in a flexible framework for evaluating biomedical ontologies in the dynamic environment of biomedicine. This framework has the potential to be extended and reused in other dynamic environments, besides biomedicine.
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