  We have created a knowledge graph based on major data sources used in
ecotoxicological risk assessment. We have applied this knowledge graph to an
important task in risk assessment, namely chemical effect prediction. We have
evaluated nine knowledge graph embedding models from a selection of geometric,
decomposition, and convolutional models on this prediction task. We show that
using knowledge graph embeddings can increase the accuracy of effect prediction
with neural networks. Furthermore, we have implemented a fine-tuning
architecture that adapts the knowledge graph embeddings to the effect
prediction task and leads to better performance. Finally, we evaluate certain
characteristics of the knowledge graph embedding models to shed light on the
individual model performance.
