Knowledge Graph Embedding for Ecotoxicological Effect Prediction
This work addresses the challenge of reducing experimental effort in ecotoxicology for researchers and laboratories, though it appears incremental as it builds on existing knowledge graph methods.
The paper tackled the problem of predicting ecotoxicological effects of chemical compounds on species by constructing a knowledge graph from public datasets and using embedding techniques, with experimental results showing it improves over selected baselines.
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.