What is it about?
One of the most challenging problems in the semantic web field consists of computing the semantic similarity between different terms. The problem here is the lack of accurate domain-specific dictionaries, such as biomedical, financial or any other particular and dynamic field. In this article we propose a new approach which uses different existing semantic similarity methods to obtain precise results in the biomedical domain. Specifically, we have developed an evolutionary algorithm which uses information provided by different semantic similarity metrics.
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Why is it important?
Our results have been validated against a variety of biomedical datasets and different collections of similarity functions.
Perspectives
The proposed system provides very high quality results when compared against similarity ratings provided by human experts (in terms of Pearson correlation coefficient) surpassing the results of other relevant works previously published in the literature.
Dr Jorge Martinez-Gil
Software Competence Center Hagenberg GmbH
Read the Original
This page is a summary of: Evolutionary algorithm based on different semantic similarity functions for synonym recognition in the biomedical domain, Knowledge-Based Systems, January 2013, Elsevier,
DOI: 10.1016/j.knosys.2012.07.005.
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