Abstract
A system for automated prediction and inference of cross-ontology links is presented. External knowledge sources are used to create a primary body of predictions. The structure of the projected super-ontology is then used to automatically infer additional predictions. Probabilistic scores are attached to all of these predictions, allowing them to be filtered using a statistically-selected threshold. Three anatomical ontologies were mapped in pairs, and all the predicted mapping links were individually checked by a manual curator, allowing a closer look at the quality of the chosen prediction procedures, and the validity of the resulting mappings.
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Ashburner, M., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 20–29 (2000)
Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32, 267–270 (2004)
de Bruijn, J., et al.: Ontology mediation, merging, and aligning. In: Davies, J., Studer, R., Warren, P. (eds.) Semantic Web Technologies, pp. 95–113. Wiley, Hoboken (2006)
Day-Richter, J.: OBO flat file format specification, version 1.2 (2006)
Diallo, G.: An effective method of large scale ontology matching. J Biomed. Seman. 5, 44 (2014). 189[PII]
Euzenat, J., Shvaiko, P.: Classifications of ontology matching techniques. In: Euzenat, J., Shvaiko, P. (eds.) Ontology Matching, pp. 73–84. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38721-0_4
Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)
Hooi, Y.K., Hassan, M.F., Shariff, A.M.: A survey on ontology mapping techniques. In: Jeong, H.Y., S. Obaidat, M., Yen, N.Y., Park, J.J.J.H. (eds.) CSA 2013. LNEE, vol. 279, pp. 829–836. Springer, Heidelberg (2014). doi:10.1007/978-3-642-41674-3_118
Miller, G.: A lexical database for English. Commun. ACM 38(11), 39–41 (1995)
van Ophuizen, E., Leunissen, J.: An evaluation of the performance of three semantic background knowledge sources in comparative anatomy. J. Integr. Bioinform. 7, 124–130 (2010)
Petrov, P., Krachunov, M., van Ophuizen, E., Vassilev, D.: An algorithmic approach to inferring cross-ontology links while mapping anatomical ontologies. Serdica J. Comput. 6, 309–332 (2012)
Petrov, P., Krachunov, M., Todorovska, E., Vassilev, D.: An intelligent system approach for integrating anatomical ontologies. Biotechnol. Biotechnol. Equipment 26(4), 3173–3181 (2012)
Rosse, C., Mejino, J.: A reference ontology for biomedical informatics: the foundational model of anatomy. J. Biomed. Inform. 36(6), 478–500 (2003)
Smith, B., et al.: The OBO foundry: coordinated evolution of ontologies to support biomedical data integration. Nat. Biotechnol. 25, 1251–1255 (2007)
Acknowledgements
This work has been supported by the National Science Fund of Bulgaria within the “Methods for Data Analysis and Knowledge Discovery in Big Sequencing Datasets” Project, Contract I02/7/2014.
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Krachunov, M., Petrov, P., Nisheva, M., Vassilev, D. (2017). Validity of Automated Inferences in Mapping of Anatomical Ontologies. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_25
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