  Extrapolation of adverse biological (toxic) effects of chemicals is an
important contribution to expand available hazard data in (eco)toxicology
without the use of animals in laboratory experiments. In this work, we
extrapolate effects based on a knowledge graph (KG) consisting of the most
relevant effect data as domain-specific background knowledge. An effect
prediction model, with and without background knowledge, was used to predict
mean adverse biological effect concentration of chemicals as a prototypical
type of stressors. The background knowledge improves the model prediction
performance by up to 40\% in terms of $R^2$ (\ie coefficient of determination).
We use the KG and KG embeddings to provide quantitative and qualitative
insights into the predictions. These insights are expected to improve the
confidence in effect prediction. Larger scale implementation of such
extrapolation models should be expected to support hazard and risk assessment,
by simplifying and reducing testing needs.
