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Abstract

In this paper we describe a novel methodology for retrieving and combining information from multiple ontologies for the medical domain. In the last decades the number and diversity of available ontologies for the medical domain has grown considerably. The variety and number of such resources available makes the cost to integrate them into an application incremental, often prohibitive for exploratory prototyping, and discouraging for larger-scale integration. Cross-ontology localized merging is proposed as a way to allow for a flexible and scalable solution. This approach also indicates a low maintenance cost and high reusability for different application types within the medical domain

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References

  1. Abdulkader, A.M.: Parallel algorithms for labelled graph matching (1998)

    Google Scholar 

  2. Bemers-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American 284(5), 34–43 (2001)

    Article  Google Scholar 

  3. Bienenstock, E., von der Malsburg, C.: A neural network for invariant pattern recognition. Europhysics Letters 4, 121 (1987)

    Article  Google Scholar 

  4. Brank, J., Grobelnik, M., Mladenic, D., Fortuna, B.: A survey of ontology evaluation techniques (2005)

    Google Scholar 

  5. Callan, J.: Distributed information retrieval. advances in information retrieval (2000)

    Google Scholar 

  6. Circuit, C.: Wikipedia (2005)

    Google Scholar 

  7. Fryer, D.: Federated search engines. Online (Weston, CT) 28(2), 16–19 (2004)

    Google Scholar 

  8. Humphreys, B.L., Lindberg, D.A.B.: The UMLS project: making the conceptual connection between users and the information they need. Bulletin of the Medical Library Association 81(2), 170 (1993)

    Google Scholar 

  9. Kalfoglou, Y., Schorlemmer, M.: Ontology mapping: the state of the art. The Knowledge Engineering Review 18(01), 1–31 (2003)

    Article  Google Scholar 

  10. Klein, M.: Combining and relating ontologies: an analysis of problems and solutions. Workshop on Ontologies and Information Sharing, IJCAI 1 (2001)

    Google Scholar 

  11. Lindberg, D.A., Humphreys, B.L., McCray, A.T.: The Unified Medical Language System. Methods Inf. Med. 32(4), 281–291 (1993)

    Google Scholar 

  12. Lipscomb, C.E.: Medical Subject Headings (MeSH). Bull Med. Libr. Assoc. 88(3), 265–266 (2000)

    Google Scholar 

  13. Moon, T.K.: The expectation-maximization algorithm. Signal Processing Magazine, IEEE 13(6), 47–60 (1996)

    Article  Google Scholar 

  14. Pedro, V.C., Eric, N., Carbonell, J.: Federated Ontology Search. In: Proceedings of the Semantic Information Integration on Knowledge Discovery (2006)

    Google Scholar 

  15. Raymond, J.W., Gardiner, E.J., Willett, P.: Rascal: Calculation of graph similarity using maximum common edge subgraphs. The Computer Journal 45(6), 631–644 (2002)

    Article  MATH  Google Scholar 

  16. Reed, S., Lenat, D.: Mapping ontologies into cyc. In: AAAI 2002 Conference Workshop on Ontologies For The Semantic Web, Edmonton, Canada, July (2002)

    Google Scholar 

  17. Sanfeliu, A., Fu, K.S.: A distance measure between attributed relational graphs for pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics 13, 353–362 (1983)

    MATH  Google Scholar 

  18. Serafini, L., Tamilin, A.:

    Google Scholar 

  19. Spackman, K.A., Campbell, K.E., Cote, R.A.: SNOMED RT: a reference terminology for health care. In: Proc. AMIA Annu. Fall. Symp., vol. 640(4), pp. 503–512 (1997)

    Google Scholar 

  20. Stumme, G., Maedche, A.: Fca-merge: Bottom-up merging of ontologies. In: 7th Intl. Conf. on Artificial Intelligence (IJCAI 2001), pp. 225–230 (2001)

    Google Scholar 

  21. Vizenor, L., Bodenreider, O., Peters, L., McCray, A.T.: Enhancing Biomedical Ontologies through Alignment of Semantic Relationships: Exploratory Approaches. In: AMIA Annu. Symp. Proc. vol. 804, p. 8 (2006)

    Google Scholar 

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Robert Meersman Zahir Tari Pilar Herrero

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© 2007 Springer-Verlag Berlin Heidelberg

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Pedro, V.C., Lita, L.V., Niculescu, S., Rao, B., Carbonell, J. (2007). Federated Ontology Search for the Medical Domain. In: Meersman, R., Tari, Z., Herrero, P. (eds) On the Move to Meaningful Internet Systems 2007: OTM 2007 Workshops. OTM 2007. Lecture Notes in Computer Science, vol 4805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76888-3_78

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  • DOI: https://doi.org/10.1007/978-3-540-76888-3_78

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76887-6

  • Online ISBN: 978-3-540-76888-3

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