Modern toxicology is evolving to leverage data science approaches to better address complex public health concerns. Understanding the adverse health impacts of exposure is a multifactorial endeavor, which requires working across a variety of data types. These data are often siloed, and integration has required manual curation and extraction. In this review, we present the utility of adopting ontologies and semantic methods to bring disparate data into a scientifically meaningful context to drive novel scientific insights. Existing semantic standards have not been widely utilized in toxicology. Broader adoption of ontologies, together with increased data sharing, will improve a researcher's ability to integrate, navigate, and analyze vast amounts of heterogeneous data—allowing for more rapid assessment of chemical(s) safety and biological mechanisms. Recent efforts have aimed to define and realize the establishment of a data ecosystem or “commons” whereby data are shared for use by all in a common infrastructure, thereby increasing the value of government-funded data sets. Investment in making data “born interoperable” using common semantics could bring computational resources to bear on issues that are solely reliant on manual and expert assessment. Here, we introduce the concept of ontologies and present our vision for computationally enabled semantic toxicology.
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