Current studentsExisting staffContact us
 

A study of concept semantic similarity measure optimization method

Simple item page
dc.contributor.authorCao, R.
dc.contributor.authorWu, L.
dc.contributor.authorWang, L.
dc.contributor.authorWang, R.
dc.contributor.authorYang, C.
dc.date.issued2016
dc.description.abstractA novel scheme based on neural networks for optimizing semantic similarity measure is proposed in this study. This scheme is investigated by using three representative neural networks, i.e., the Back Propagation (BP) neural networks optimized by Genetic Algorithm (GA), the BP neural networks optimized by the Mind Evolutionary Algorithm (MEA), and the generalized regression neural network (GRNN). The performance of our proposed scheme is examined on the problem of semantic similarity measure for Special Crop. To this end, the Special Crops domain ontology is established and a set of factors having major influence on semantic similarity measure for neural networks training process is identified. Extensive experiments and comparison demonstrate that optimizing semantic similarity measure based on neural network is both feasible and effective. The promising experimental results also suggest that the method based on the generalized regression neural network performs the best.
dc.identifier.citationJournal of Computational and Theoretical Nanoscience, 2016; 13(1):547-557
dc.identifier.doi10.1166/jctn.2016.4839
dc.identifier.issn1546-1955
dc.identifier.issn1546-1963
dc.identifier.urihttps://hdl.handle.net/11541.2/123601
dc.language.isoen
dc.publisherAmerican Scientific Publishers
dc.rightsCopyright 2016 American Scientific Publishers
dc.source.urihttps://doi.org/10.1166/jctn.2016.4839
dc.subjectcrops domain
dc.subjectoptimization method
dc.subjectspecial crops domain ontology
dc.titleA study of concept semantic similarity measure optimization method
dc.typeJournal article
pubs.publication-statusPublished
ror.mmsid9916105206301831

Files

Collections