Biomedical Ontology Matching as a Service
Ontology matching is among the core techniques used for integration and interoperability resolution between biomedical systems. However, due to the excess usage and ever-evolving nature of biomedical data, ontologies are becoming large-scale, and complex; consequently, requiring scalable computational environments with performance and availability in mind. In this paper, we present a cloud-based ontology matching system for biomedical ontologies that provides ontology matching as a service. Our proposed system implements parallelism at various levels to improve the overall ontology matching performance especially for large-scale biomedical ontologies and incorporates third-party resources UMLS and Wordnet for comprehensive matched results. Matched results are delivered to the service consumer as bridge ontology and preserved in ubiquitous ontology repository for future request. We evaluate our system by consuming the matching service in an interoperability engine of a clinical decision support system (CDSS), which generates mapping requests for FMA and NCI biomedical ontologies.
KeywordsBiomedical ontologies Ontology matching Cloud computing Software as a service
This research was supported by Microsoft Research Asia, Beijing, China, under the research grant provided as MSRA Project Award 2013–2014 and MSIP(Ministry of Science, ICT&Future Planning), Korea, under IT/SW Creative research program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2013-(H0503-13-1010).
This research was also supported by Microsoft Azure4Research Award 2013–2014.
- 1.López-Fernández, H., Reboiro-Jato, M., Glez-Pea, D., Aparicio, F., Gachet, D., Buenaga, M., Fdez-Riverola, F.: BioAnnote: a software platform for annotating biomedical documents with application in medical learning environments. Comput. Methods Programs Biomed. 111, 139–147 (2013)CrossRefGoogle Scholar
- 2.Cimino, J., Zhu, X.: IMIA Yearbook of Medical 1, 124–135 (2006)Google Scholar
- 8.Schulz, S., Cornet, R., Spackman, K.: Consolidating SNOMED CT’s ontological commitment. Appl. Ontol. 1, 1–11 (2011)Google Scholar
- 9.Smith, B., Ashburner, M., Rosse, C., Bard, J., Bug, W., Ceusters, W., Goldberg, L.J., Eilbeck, K., Ireland, A., Mungall, C.J., OBI Consortium, Leontis, N., Rocca-Serra, P., Ruttenberg, A., Sansone, S.A., Scheuermann, R.H., Shah, N., Whetzel P.L., Lewis, S.: The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat. Biotech 25, 1251–1255 (2007)Google Scholar
- 10.Jimnez-Ruiz, E., Meilicke, C., Cuenca Grau, B., Horrocks, I.: Evaluating mapping repair systems with large biomedical ontologies. In: 26th International Workshop on Description Logics. LNCS. Springer (2013)Google Scholar
- 12.Gennari, J.H., Silberfein, A.: Leveraging an alignment between two large ontologies: FMA and GO. In: Seventh International Protege Conference (2004)Google Scholar
- 15.Schuyler, P.L., Hole, W.T., Tuttle, M.S., Sherertz, D.D.: The UMLS Metathesaurus: representing different views of biomedical concepts. Bull. Med. Libr. Assoc. 81, 217–222 (1993)Google Scholar
- 16.Princeton University, What is WordNet? (2013)Google Scholar
- 19.National Center for Biotechnology Information, U.S. National Library of Medicine, PubMed (2013)Google Scholar
- 25.Kirsten, T., Kolb, L., Hartung, M., Gross, A., Köpcke, H., Rahm, E.: Data partitioning for parallel entity matching. In: 8th International Workshop on Quality in Databases (2010)Google Scholar