Abstract
Ontologies, as representation of shared conceptualization for variety of specific domains, are the heart of the Semantic Web. In order to facilitate interoperability across multiple ontologies, we need an automatic mechanism to align ontologies. Therefore, many methods to measure similarity between concepts existing in two different ontologies are proposed. In this paper, we will enumerate these methods along with their shortcomings in each case. In information content (IC) based similarity measures, the process of IC computation for concepts is so challenging and in many cases with failing. We will propose our new approach that is based on concepts’ definitions. These definitions would help us to compute reliable and easy to calculate information contents for concepts. Applying these methods to the biomedical domain, using MEDLINE as corpus, International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9CM) as thesaurus, and available reference standard, we will find our method outperforms other similarity measures.
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Pesaranghader, A., Muthaiyah, S. (2013). Definition-based Information Content Vectors for Semantic Similarity Measurement. In: Noah, S.A., et al. Soft Computing Applications and Intelligent Systems. M-CAIT 2013. Communications in Computer and Information Science, vol 378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40567-9_23
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DOI: https://doi.org/10.1007/978-3-642-40567-9_23
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