  Exploring the effects a chemical compound has 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. In this paper 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 classification and similarity. The publicly available
effect data is integrated to the knowledge graph using ontology alignment
techniques. Our experimental results show that the knowledge graph based
approach improves the selected baselines.
