City Research Online

Abstract

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 which adapts the knowledge graph embeddings to the effect prediction task and leads to a better performance. Finally, we evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.

Publication Type:	Article
Additional Information:	© 2022 – The authors. Published by IOS Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).
Publisher Keywords:	Knowledge graph, ecotoxicology, risk assessment, adverse effects, embedding, chemicals, species
Subjects:	Q Science > QD Chemistry Q Science > QH Natural history > QH301 Biology
Departments:	School of Science & Technology > Computer Science
SWORD Depositor:	Symplectic Administrator

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