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Knowledge Graph Embedding for Ecotoxicological Effect Prediction

Myklebust, E., Jimenez-Ruiz, E. ORCID: 0000-0002-9083-4599, Chen, J. , Wolf, R. & Tollefsen, K. E. (2019). Knowledge Graph Embedding for Ecotoxicological Effect Prediction. In: The Semantic Web – ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings. Lecture Notes in Computer Science, 11779 (11778). (pp. 490-506). Cham: Springer. ISBN 978-3-030-30792-9 doi: 10.1007/978-3-030-30793-6


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.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © Springer Nature Switzerland AG. The final authenticated publication is available online at
Publisher Keywords: Knowledge graph, Semantic embedding, Ecotoxicology
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Departments: School of Science & Technology > Computer Science
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