Exploring the effects of a chemical compound on a species takes a considerable experimental effort. Appropriate methods for estimating and suggesting new effects can dramatically reduce the work needed to be done by a laboratory. Here, we explore the suitability of using a knowledge graph embedding approach for ecotoxicological effect prediction. A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical knowledge. These knowledge sources are integrated by ontology alignment techniques. Our experimental results show that the knowledge graph and its embeddings augment the baseline models.
This item's license is: Attribution 4.0 International