Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings

Tracking #: 2804-4018

This paper is currently under review
Authors: 
Erik Bryhn Myklebust
Ernesto Jimenez-Ruiz
Jiaoyan Chen
Raoul Wolf
Knut Erik Tollefsen

Responsible editor: 
Guest Editors DeepL4KGs 2021

Submission type: 
Full Paper
Abstract: 
Semantic Web technologies enable the interoperability of disparate data sources. We have created a knowledge graph based on major data sources used in ecotoxicological risk assessment. This facilitates the use of the extensive library of Semantic Web tools. 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.
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