Knowledge graph embedding for ecotoxicological effect prediction
Journal article, Peer reviewed
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Original versionCEUR Workshop Proceedings. 2019, 2456, 37-40.
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.