A Survey of Systematic Evidence Mapping Practice and the Case for Knowledge Graphs in Environmental Health and Toxicology

Abstract

Abstract Systematic evidence mapping offers a robust and transparent methodology for facilitating evidence-based approaches to decision-making in chemicals policy and wider environmental health (EH). Interest in the methodology is growing; however, its application in EH is still novel. To facilitate the production of effective systematic evidence maps for EH use cases, we survey the successful application of evidence mapping in other fields where the methodology is more established. Focusing on issues of “data storage technology,” “data integrity,” “data accessibility,” and “transparency,” we characterize current evidence mapping practice and critically review its potential value for EH contexts. We note that rigid, flat data tables and schema-first approaches dominate current mapping methods and highlight how this practice is ill-suited to the highly connected, heterogeneous, and complex nature of EH data. We propose this challenge is overcome by storing and structuring data as “knowledge graphs.” Knowledge graphs offer a flexible, schemaless, and scalable model for systematically mapping the EH literature. Associated technologies, such as ontologies, are well-suited to the long-term goals of systematic mapping methodology in promoting resource-efficient access to the wider EH evidence base. Several graph storage implementations are readily available, with a variety of proven use cases in other fields. Thus, developing and adapting systematic evidence mapping for EH should utilize these graph-based resources to ensure the production of scalable, interoperable, and robust maps to aid decision-making processes in chemicals policy and wider EH.