TERA: the Toxicological Effect and Risk Assessment Knowledge Graph

TL;DR

The paper introduces TERA, a comprehensive knowledge graph that integrates chemical effect data to facilitate ecological risk assessment and support chemical effect prediction.

Contribution

It presents the construction of the TERA knowledge graph and demonstrates its applications in chemical effect prediction and interoperability within the Semantic Web.

Findings

TERA enables integrated chemical effect data access.

Application of TERA improves chemical effect prediction accuracy.

Supports interoperability for ecological risk assessment data.

Abstract

Ecological risk assessment requires large amounts of chemical effect data from laboratory experiments. Due to experimental effort and animal welfare concerns it is desired to extrapolate data from existing sources. To cover the required chemical effect data several data sources need to be integrated to enable their interoperability. In this paper we introduce the Toxicological Effect and Risk Assessment (TERA) knowledge graph, which aims at providing such integrated view, and the data preparation and steps followed to construct this knowledge graph. We also present the applications of TERA for chemical effect prediction and the potential applications within the Semantic Web community.