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1000
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Abstract/Summary
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Healthcare data, such as patients’ symptoms, laboratory test results, and various clinical measurements are temporal in nature, and are associated with a time. Modelling temporal healthcare data could benefit healthcare practitioners in healthcare decision making and support patient care. One method for modelling data that has been used in academia and industry is RDF-based knowledge graphs (KGs). Many approaches have been proposed to model temporal data in RDF-based (KGs) which have not been evaluated systematically in the healthcare domain. In this paper, an evaluation framework is proposed for the evaluation of temporal data modeling approaches in KGs and has been applied in a healthcare use case.
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