City Research Online

Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings

Myklebust, E. B., Jimenez-Ruiz, E. ORCID: 0000-0002-9083-4599, Chen, J. , Wolf, R. & Tollefsen, K. E. (2022). Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings. Semantic Web, 13(3), pp. 299-338. doi: 10.3233/SW-222804


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:
[thumbnail of sw_2022_13-3_sw-13-3-sw222804_sw-13-sw222804.pdf]
Text - Published Version
Available under License Creative Commons: Attribution International Public License 4.0.

Download (1MB) | Preview


Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email


Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login