Home > Research > Publications & Outputs > Knowledge Organization Systems for Systematic C...

Electronic data

  • EHP6994

    Final published version, 1.47 MB, PDF document

    Available under license: Other

Links

Text available via DOI:

View graph of relations

Knowledge Organization Systems for Systematic Chemical Assessments

Research output: Contribution to journalJournal articlepeer-review

Published
Close
<mark>Journal publication date</mark>24/12/2020
<mark>Journal</mark>Environmental Health Perspectives
Issue number12
Volume128
Pages (from-to)125001
Publication StatusPublished
<mark>Original language</mark>English

Abstract

BACKGROUND: Although the implementation of systematic review and evidence mapping methods stands to improve the transparency and accuracy of chemical assessments, they also accentuate the challenges that assessors face in ensuring they have located and included all the evidence that is relevant to evaluating the potential health effects an exposure might be causing. This challenge of information retrieval can be characterized in terms of "semantic" and "conceptual" factors that render chemical assessments vulnerable to the streetlight effect.

OBJECTIVES: This commentary presents how controlled vocabularies, thesauruses, and ontologies contribute to overcoming the streetlight effect in information retrieval, making up the key components of Knowledge Organization Systems (KOSs) that enable more systematic access to assessment-relevant information than is currently achievable. The concept of Adverse Outcome Pathways is used to illustrate what a general KOS for use in chemical assessment could look like.

DISCUSSION: Ontologies are an underexploited element of effective knowledge organization in the environmental health sciences. Agreeing on and implementing ontologies in chemical assessment is a complex but tractable process with four fundamental steps. Successful implementation of ontologies would not only make currently fragmented information about health risks from chemical exposures vastly more accessible, it could ultimately enable computational methods for chemical assessment that can take advantage of the full richness of data described in natural language in primary studies. https://doi.org/10.1289/EHP6994.