Advertisement

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)

Abstract

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.

Keywords

Knowledge graph Semantic embedding Ecotoxicology 

Notes

Acknowledgements

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.

References

  1. 1.
    Agibetov, A., et al.: Supporting shared hypothesis testing in the biomedical domain. J. Biomed. Semant. 9(1), 9:1–9:22 (2018)CrossRefGoogle Scholar
  2. 2.
    Agibetov, A., Samwald, M.: Global and local evaluation of link prediction tasks with neural embeddings. In: 4th Workshop on Semantic Deep Learning (ISWC Workshop), pp. 89–102 (2018)Google Scholar
  3. 3.
    Alshahrani, M., Khan, M.A., Maddouri, O., Kinjo, A.R., Queralt-Rosinach, N., Hoehndorf, R.: Neuro-symbolic representation learning on biological knowledge graphs. Bioinformatics 33(17), 2723–2730 (2017)CrossRefGoogle Scholar
  4. 4.
    Arnaout, H., Elbassuoni, S.: Effective searching of RDF knowledge graphs. Web Semant.: Sci. Serv. Agents World Wide Web 48 (2018)CrossRefGoogle Scholar
  5. 5.
    Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems 26, pp. 2787–2795. Curran Associates, Inc. (2013)Google Scholar
  6. 6.
    ChEBI-ontology: The European bioinformatics institute (2019). https://www.ebi.ac.uk/chebi/
  7. 7.
    Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras
  8. 8.
    Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNetzbMATHGoogle Scholar
  9. 9.
    US EPA: Ecotoxicology knowledgebase (ECOTOX) (2019). https://cfpub.epa.gov/ecotox/
  10. 10.
    Euzenat, J., Shvaiko, P.: Ontology Matching, 2nd edn. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-38721-0CrossRefzbMATHGoogle Scholar
  11. 11.
    Jiménez-Ruiz, E., Cuenca Grau, B.: LogMap: logic-based and scalable ontology matching. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 273–288. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25073-6_18CrossRefGoogle Scholar
  12. 12.
    Jimenez-Ruiz, E., Cuenca Grau, B., Zhou, Y., Horrocks, I.: Large-scale interactive ontology matching: algorithms and implementation. In: The 20th European Conference on Artificial Intelligence (ECAI), pp. 444–449. IOS Press (2012)Google Scholar
  13. 13.
    Kadlec, R., Bajgar, O., Kleindienst, J.: Knowledge base completion: baselines strike back. CoRR abs/1705.10744 (2017). http://arxiv.org/abs/1705.10744
  14. 14.
    Lehmann, J., et al.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015)Google Scholar
  15. 15.
    Myklebust, E.B., Jimenez-Ruiz, E., Rudjord, Z.C., Wolf, R., Tollefsen, K.E.: Integrating semantic technologies in environmental risk assessment: a vision. In: 29th Annual Meeting of the Society of Environmental Toxicology and Chemistry (SETAC) (2019)Google Scholar
  16. 16.
    NCBI-Taxonomy: The national center for biotechnology information (2019). https://www.ncbi.nlm.nih.gov/taxonomy
  17. 17.
    Nickel, M., Rosasco, L., Poggio, T.A.: Holographic embeddings of knowledge graphs. CoRR abs/1510.04935 (2015). http://arxiv.org/abs/1510.04935
  18. 18.
    Nikolova, N., Jaworska, J.: Approaches to measure chemical similarity – a review. QSAR Comb. Sci. 22(9–10), 1006–1026 (2003)CrossRefGoogle Scholar
  19. 19.
    Pradeep, P., Povinelli, R.J., White, S., Merrill, S.J.: An ensemble model of QSAR tools for regulatory risk assessment. J. Cheminform. 8, 48 (2016)CrossRefGoogle Scholar
  20. 20.
    PubChem: National Institutes of Health (NIH) (2019). https://pubchem.ncbi.nlm.nih.gov/
  21. 21.
    Vrandecic, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)CrossRefGoogle Scholar
  22. 22.
    Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)CrossRefGoogle Scholar
  23. 23.
    Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. CoRR abs/1412.6575 (2015)Google Scholar

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

Personalised recommendations