A Usable Knowledge Graph Framework for Linking Health Events with Environmental Data

Abstract

Environmental exposures transported across air, land and water can affect our health making us more susceptible to developing a disease. Researchers studying these health-environment interactions integrate and link multiple and diverse data sources as part of their research workflows. Emerging technologies such as Knowledge Graphs (KG) can make the data integration process efficient for researchers by making the datasets interoperable. However, KG technologies are not easy to incorporate into researchers? workflows due to the technical knowledge and practical expertise required to access, explore and establish relevant links between the datasets. The major contribution of this PhD thesis is the proposed framework SERDIF (Semantic Environmental and Rare Disease data Integration Framework) that allows health data researchers themselves to directly link health data with relevant environmental data in support of their research workflows. SERDIF advances the state of the art in being the first usable KG framework, that is W3C standards-based, to be developed and implemented for the study of environmental triggers associated with rare diseases. This PhD thesis yielded two minor contributions towards improving the adoption of KG technologies and promoting transparency of research methods and data reuse towards improving the efficiency of scientific research. The first minor contribution is a step by step description of the methods and results of the evaluation approach, providing KG practitioners with a reproducible example in how to make their technologies usable for domain experts. The second minor contribution is a a collection of open source artefacts as a by-product during the development of SERDIF published to promote open science. While SERDIF has been implemented for rare disease studies, the framework has the potential to be used in other contexts to address the data integration challenges of environmental studies.