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A major obstacle to the wider use of semantic technology is the perceived complexity of RDF data by stakeholders who are not familiar with the Linked Data paradigm, or are otherwise unaware of a dataset’s underlying schema. In order to help overcome this barrier, we propose the ExConQuer Framework (Explore, Convert, and Query Framework) as a tool that preserves the semantic richness of the data model while catering for simplified and workable views of the data. With the aim of encouraging and enabling further re-use of Linked Data by people who would otherwise shy away from this task, this framework facilitates the publication and consumption of RDF in a variety of generic formats. In this manner, any stakeholder can export and work with RDF data in the formats they are most accustomed with, radically lowering the entry barrier to the use of semantic technologies, and possibly enabling the exploitation of Linked Data to its full potential. Through the ExConQuer Framework we provide a comprehensive set of tools that enable users to easily query linked datasets, download the results in a number of formats, and re-use previously-executed queries and transformations. With this framework we hence attempt to target the evident niche in existing tools that are intended to be used by non-experts to consume Linked Data.

Environmental exposures transported across air, land and water can affect our health making us more susceptible to developing a disease. Therefore, researchers need to face the complex task of integrating environmental exposures and linking them to health events with the relevant spatiotemporal and health context for individuals or populations. We present a usability evaluation approach and study of a semantic framework (i.e. Knowledge Graph, Methodology and User Interface) to enable Health Data Researchers (HDR) to link particular health events with environmental data for rare disease research. The usability study includes 17 HDRs with expertise in health data related to Anti-Neutrophil Cytoplasmic Antibody (ANCA)-associated vasculitis (AAV) in Ireland and Kawasaki Disease in Japan, and with no previous practical experience in using Semantic Web (SW) technologies. The evaluation results are promising in that they indicate that the framework is useful in allowing researchers themselves to link health and environmental data whilst hiding the complexities of SW technologies. As a result of this work, we also discuss the limitations of the approach together with the applicability to other domains. Beyond the direct impact on environmental health studies, the description of the evaluation approach can guide researchers in making SW technologies more accessible to domain experts through usability studies.