Paper Info
Title
Evolutionary algorithm based on different semantic similarity functions for synonym recognition in the biomedical domain
Abstract
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. Our results have been validated against a variety of biomedical datasets and different collections of similarity functions. 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.
Year
DOI
Venue
2013
10.1016/j.knosys.2012.07.005
Knowl.-Based Syst.
Keywords
Field
DocType
biomedical domain,different term,semantic similarity,semantic web field,different semantic similarity function,similarity rating,similarity function,different semantic similarity metrics,synonym recognition,different collection,evolutionary algorithm,biomedical datasets,different existing semantic similarity,semantic web,evolutionary computation,differential evolution
Semantic similarity,Data mining,Pearson product-moment correlation coefficient,Evolutionary algorithm,Information retrieval,Computer science,Similarity heuristic,Semantic Web,Evolutionary computation,Differential evolution,Semantic computing
Journal
Volume
ISSN
Citations 
37,
0950-7051
11
PageRank 
References 
Authors
0.50
28
2
Name
Order
Citations
PageRank
José M. Chaves-gonzález111912.16
Jorge MartíNez-Gil2253.38