Abstract
Biomedical ontologies have become a mainstream topic in medical research. They represent important sources of evolved knowledge that may be automatically integrated in decision support methods. Grounding clinical and radiographic findings in concepts defined by a biomedical ontology, e.g., the Human Phenotype Ontology, enables us to compute semantic similarity between them. In this paper, we focus on using such similarity measures to predict disorders on undiagnosed patient cases in the bone dysplasia domain. Different methods for computing the semantic similarity have been implemented. All methods have been evaluated based on their support in achieving a higher prediction accuracy. The outcome of this research enables us to understand the feasibility of developing decision support methods based on ontology-driven semantic similarity in the skeletal dysplasia domain.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Groza, T., Zankl, A., Li, Y.-F., Hunter, J.: Using Semantic Web Technologies to Build a Community-Driven Knowledge Curation Platform for the Skeletal Dysplasia Domain. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part II. LNCS, vol. 7032, pp. 81–96. Springer, Heidelberg (2011)
Robinson, P.N., Kohler, S., Bauer, S., Seelow, D., Horn, D., Mundlos, S.: The Human Phenotype Ontology: A Tool for Annotating and Analyzing Human Hereditary Disease. The American Journal of Human Genetics 83(5), 610–615 (2008)
Groza, T., Hunter, J., Zankl, A.: The Bone Dysplasia Ontology: integrating genotype and phenotype information in the skeletal dysplasia domain. BMC Bioinformatics 13(50) (2012)
Pesquita, C., Faria, D., Falcao, A., Lord, P., Couto, F.: Semantic Similarity in Biomedical Ontologies. PLoS Computational Biology 5(7) (2009)
Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proc. of the 14th IJCAI, pp. 448–453 (1995)
Lin, D.: An information-theoretic definition of similarity. In: Proc. of the 15th ICML, pp. 296–304 (1998)
Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proc. of the 10th Conf. on Research on Comp, Linguistics, Taiwan (1997)
Wu, Z., Palmer, M.: Verb semantics and lexicon selection. In: Proc. of the 32nd ACL, pp. 133–138 (1994)
Chodorow, M., Leacock, C.: Combining local context and WordNet similarity for word sense identification. Fellbaum, 265–283 (1997)
Schickel-Zuber, V., Faltings, B.: OSS: A Semantic Similarity Function based on Hierarchical Ontologies. In: Proc. of the 20th IJCAI, pp. 551–556 (2007)
Li, Y., Bandar, Z., McLean, D.: An approach for measuring semantic similarity between words using multiple information sources. ITEE Transactions on Knowledge and Data Engineering 15(4), 871–882 (2003)
Pirró, G., Euzenat, J.: A Feature and Information Theoretic Framework for Semantic Similarity and Relatedness. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 615–630. Springer, Heidelberg (2010)
Seco, N., Veale, T., Hayes, J.: An Intrinsic Information Content measure for Semantic Similarity in WordNet. In: Proc. of ECAI 2004, pp. 1089–1090 (2004)
Kohler, S., Schulz, M.H., Krawitz, P., Bauer, S., Dolken, S., Ott, C.E., Mundlos, C., Horn, D., Mundlos, S., Robinson, P.N.: Clinical diagnostics in human genetics with semantic similarity searches in ontologies. The American Journal of Human Genetics 85(4), 457–464 (2009)
Tao, Y., Sam, L., Li, J., Friedman, C., Lussier, Y.A.: Information theory applied to the sparse gene ontology annotation network to predict novel gene function. Bioinformatics 23(13), i529–i538 (2007)
Berardini, T.Z., et al.: The Gene Ontology in 2010: extensions and refinements. Nucleic Acids Research 38, D331–D335 (2010)
Lei, Z., Dai, Y.: Assessing protein similarity with Gene Ontology and its use in subnuclear localization prediction. BMC Bioinformatics 7(1), 491 (2006)
Lord, P.W., Stevens, R.D., Brass, A., Goble, C.A.: Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics 19(10), 1275–1283 (2003)
Washington, N.L., Haendel, M.A., Mungall, C.J., Ashburner, M., Westerfield, M., Lewis, S.E.: Linking human diseases to animal models using ontology-based phenotype annotation. PLoS Biology 7(11) (2009)
Ferreira, J.D., Couto, F.M.: Semantic similarity for automatic classification of chemical compounds. PLoS Computational Biology 6(9) (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Paul, R., Groza, T., Zankl, A., Hunter, J. (2012). Semantic Similarity-Driven Decision Support in the Skeletal Dysplasia Domain. In: Cudré-Mauroux, P., et al. The Semantic Web – ISWC 2012. ISWC 2012. Lecture Notes in Computer Science, vol 7650. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35173-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-35173-0_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35172-3
Online ISBN: 978-3-642-35173-0
eBook Packages: Computer ScienceComputer Science (R0)