Knowledge Graph Embedding for Ecotoxicological Effect Prediction

  • Erik B. MyklebustEmail author
  • Ernesto Jimenez-Ruiz
  • Jiaoyan Chen
  • Raoul Wolf
  • Knut Erik Tollefsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11779)


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.


Knowledge graph Semantic embedding Ecotoxicology 



This work is supported by the grant 272414 from the Research Council of Norway (RCN), the MixRisk project (RCN 268294), the AIDA project, The Alan Turing Institute under the EPSRC grant EP/N510129/1, the SIRIUS Centre for Scalable Data Access (RCN 237889), the Royal Society, EPSRC projects DBOnto, \(\text {MaSI}^{\text {3}}\) and \(\text {ED}^{\text {3}}\). We would also like to thank Martin Giese and Zofia C. Rudjord for their contribution in early stages of this project.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Erik B. Myklebust
    • 1
    • 2
    Email author
  • Ernesto Jimenez-Ruiz
    • 2
    • 3
  • Jiaoyan Chen
    • 4
  • Raoul Wolf
    • 1
  • Knut Erik Tollefsen
    • 1
  1. 1.Norwegian Institute for Water ResearchOsloNorway
  2. 2.Department of InformaticsUniversity of OsloOsloNorway
  3. 3.Alan Turing InstituteLondonUK
  4. 4.Department of Computer ScienceUniversity of OxfordOxfordUK

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