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.
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- 1.
Chemical and compound are used interchangeably.
- 2.
NIVA Institute: https://www.niva.no/en.
- 3.
NIVA Risk Assessment Database: https://www.niva.no/en/projectweb/radb.
- 4.
The OWL 2 ontology language provides more expressive constructors. Note that the graph projection of an OWL 2 ontology can be seen as a knowledge graph (e.g., [1]).
- 5.
The models are implemented with Keras [7]. Data and codes available from: https://github.com/Erik-BM/NIVAUC.
References
Agibetov, A., et al.: Supporting shared hypothesis testing in the biomedical domain. J. Biomed. Semant. 9(1), 9:1–9:22 (2018)
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)
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)
Arnaout, H., Elbassuoni, S.: Effective searching of RDF knowledge graphs. Web Semant.: Sci. Serv. Agents World Wide Web 48 (2018)
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)
ChEBI-ontology: The European bioinformatics institute (2019). https://www.ebi.ac.uk/chebi/
Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)
US EPA: Ecotoxicology knowledgebase (ECOTOX) (2019). https://cfpub.epa.gov/ecotox/
Euzenat, J., Shvaiko, P.: Ontology Matching, 2nd edn. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38721-0
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_18
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)
Kadlec, R., Bajgar, O., Kleindienst, J.: Knowledge base completion: baselines strike back. CoRR abs/1705.10744 (2017). http://arxiv.org/abs/1705.10744
Lehmann, J., et al.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015)
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)
NCBI-Taxonomy: The national center for biotechnology information (2019). https://www.ncbi.nlm.nih.gov/taxonomy
Nickel, M., Rosasco, L., Poggio, T.A.: Holographic embeddings of knowledge graphs. CoRR abs/1510.04935 (2015). http://arxiv.org/abs/1510.04935
Nikolova, N., Jaworska, J.: Approaches to measure chemical similarity – a review. QSAR Comb. Sci. 22(9–10), 1006–1026 (2003)
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)
PubChem: National Institutes of Health (NIH) (2019). https://pubchem.ncbi.nlm.nih.gov/
Vrandecic, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)
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)
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)
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.
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Myklebust, E.B., Jimenez-Ruiz, E., Chen, J., Wolf, R., Tollefsen, K.E. (2019). Knowledge Graph Embedding for Ecotoxicological Effect Prediction. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11779. Springer, Cham. https://doi.org/10.1007/978-3-030-30796-7_30
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DOI: https://doi.org/10.1007/978-3-030-30796-7_30
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