Knowledge Graph Embedding for Ecotoxicological Effect Prediction

Exploring the effects a chemical compoundhas 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 embeddingapproach for ecotoxicologicaleffect prediction. A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classificationand similarity. The publicly available effect data is integrated to the knowledge graph using ontology alignmenttechniques. Our experimental results show that the knowledge graph based approach improves the selected baselines.