Biomedical Ontology Matching as a Service

  • Muhammad Bilal AminEmail author
  • Mahmood Ahmad
  • Wajahat Ali Khan
  • Sungyoung Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8456)


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.


Biomedical 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. 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. 2.
    Cimino, J., Zhu, X.: IMIA Yearbook of Medical 1, 124–135 (2006)Google Scholar
  3. 3.
    Isern, D., Snchez, D., Moreno, A.: Ontology-driven execution of clinical guidelines. Comput. Methods Programs Biomed. 107, 122–139 (2012)CrossRefGoogle Scholar
  4. 4.
    De Potter, P., Cools, H., Depraetere, K., Mels, G., Debevere, P., De Roo, J., Huszka, C., Colaert, D., Mannens, E., Van de Walle, R.: Semantic patient information aggregation and medicinal decision support. Comput. Methods Programs Biomed. 2, 724–735 (2012)CrossRefGoogle Scholar
  5. 5.
    Gene Ontology Consortium: The Gene Ontology (GO) database and informatics resource. Nucleic Acid Res. (Database issue) 32, D258–D261 (2004)CrossRefGoogle Scholar
  6. 6.
    Golbeck, J., Fragoso, G., Hartel, F., Hendler, J., Oberthaler, J., Parsia, B.: The National Cancer Institute’s Thesaurus and ontology. Web Semant. Sci. Serv. Agents World Wide Web 1, 75–80 (2003)CrossRefGoogle Scholar
  7. 7.
    Rosse, C., Mejino, J.L.: A reference ontology for biomedical informatics. J. Biomed. Inform. 36, 478–500 (2003)CrossRefGoogle Scholar
  8. 8.
    Schulz, S., Cornet, R., Spackman, K.: Consolidating SNOMED CT’s ontological commitment. Appl. Ontol. 1, 1–11 (2011)Google Scholar
  9. 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. 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
  11. 11.
    Sun, X., Li, J.: pairheatmap: comparing expression profiles of gene groups in heatmaps. Comput. Methods Programs Biomed. 112, 599–606 (2013)CrossRefGoogle Scholar
  12. 12.
    Gennari, J.H., Silberfein, A.: Leveraging an alignment between two large ontologies: FMA and GO. In: Seventh International Protege Conference (2004)Google Scholar
  13. 13.
    Khan, W.A., Hussain, M., Afzal, M., Amin, M.B., Saleem, M.A., Lee, S.: Personalized-detailed clinical model for data interoperability among clinical standards. Telemed. e-Health 19, 632–642 (2013)CrossRefGoogle Scholar
  14. 14.
    Gross, A., Hartung, M., Kirsten, T., Rahm, E.: On matching large life science ontologies in parallel. In: Lambrix, P., Kemp, G. (eds.) DILS 2010. LNCS, vol. 6254, pp. 35–49. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 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. 16.
    Princeton University, What is WordNet? (2013)Google Scholar
  17. 17.
    Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25, 158–176 (2013)CrossRefGoogle Scholar
  18. 18.
    Lambrix, P., Tan, H.: SAMBO-A system for aligning and merging biomedical ontologies. Web Semant. 4, 196–206 (2006)CrossRefGoogle Scholar
  19. 19.
    National Center for Biotechnology Information, U.S. National Library of Medicine, PubMed (2013)Google Scholar
  20. 20.
    Jean-Mary, Y.R., Shironoshita, E.P., Kabuka, M.R.: Ontology matching with semantic verification. Web Semant. 7, 235–251 (2009)CrossRefGoogle Scholar
  21. 21.
    Ba, M., Diallo, G.: Large-scale biomedical ontology matching with ServOMap. IRBM 34, 56–59 (2011)CrossRefGoogle Scholar
  22. 22.
    Cruz, I.F., Antonelli, F.P., Stroe, C.: AgreementMaker: efficient matching for large real-world schemas and ontologies. Proc. VLDB Endow. 2, 1586–1589 (2009)CrossRefGoogle Scholar
  23. 23.
    Jiménez-Ruiz, E., Cuenca Grau, B.: LogMap: logic-based and scalable ontology matching. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 273–288. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  24. 24.
    Kirsten, T., Gross, A., Hartung, M., Rahm, E.: GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution. J. Biomed. Semant. 2, 6 (2011)CrossRefGoogle Scholar
  25. 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

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Muhammad Bilal Amin
    • 1
    Email author
  • Mahmood Ahmad
    • 1
  • Wajahat Ali Khan
    • 1
  • Sungyoung Lee
    • 1
  1. 1.Department of Computer EngineeringKyung Hee UniversitySeoulSouth Korea

Personalised recommendations