Towards an understanding of the semantic web opportunities, usages, affordances and challenges in health research

Abstract

The semantic web (SW) offers tools for supporting data integration and sharing across disparate resources in the web. Meanwhile, health research needs an efficient approach for handling heterogenous data integration for the massive amounts of available health-related data to help discovering new scientific breakthroughs. In this thesis, the current and potential relationships between the semantic web and health research are aimed to be understood and identified through systematically reviewing the literature and examining the SW features in a proof-ofconcept health-related demonstrator. Firstly, a systematic literature review of 447 articles addressing health questions and using the SW standards was conducted to map the literature and identify any gaps or opportunities. The results of the review were analysed in a mixed approach of quantitative and qualitative methods producing two taxonomies: 1) the health aims and 2) the SW features taxonomies. The review revealed the most and least addressed health questions as well as the used SW features in the literature. Secondly, a semantic web-based demonstrator was developed to represent the NHS dispensed prescriptions topic and examine some of the identified SW features. The prescriptions demonstrator consists of three interlinked OWL ontologies: the BNF, NHS and prescriptions ontologies along with their converted RDF instances. Moreover, two health questions, inspired from the traditional health literature and suggested by health experts in a focus group, were translated into SPARQL queries and ran across the ontologies to test more of the SW features. It has been learned that the SW has a potential in supporting health research and accelarting research findings in the areas of: data representaion, data integration and knowledge discovery. However, there are some challenges need resolving for a better result such as: data accessibility, security, quality, heterogeneity and lack of user-friendly tools.

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ORCID for Mark Weal: orcid.org/0000-0001-6251-8786

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