@inproceedings{Xia2009,
abstract = {To improve accuracy of semantic similarity measure between ontology concepts, four main factors that impact on semantic similarity measure is taken into account. They are semantic distance, semantic depth, semantic coincidence and semantic density. Firstly, they were preprocessed to obtain four basic methods for calculating semantic similarity. And then Multi Expression Programming algorithm was adopted to combine and optimise the four basic methods. Thus, an approach of semantic similarity measure between ontology concepts based on Multi Expression Programming is proposed. At last, the approach is tested using dataset extracted from WordNet. The experiment result shows that the approach can be able to exclude the influence of non-key factor and enhance accuracy of semantic similarity measure.},
author = {Xia, Shixiong and Hu, Zuhui and Niu, Qiang},
booktitle = {Sixth Web Information Systems and Applications Conference WISA 2009},
doi = {10.1109/WISA.2009.34},
keywords = {SML-LIB-BIBLIO,genetic algorithms,genetic programming,lang:ENG,mathematical programming,multiexpression programming,ontologies (artificial intelligence),ontology concepts,semantic coincidence,semantic density,semantic depth,semantic distance,semantic similarity measure},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
pages = {184--188},
title = {{An Approach of Semantic Similarity Measure between Ontology Concepts Based on Multi Expression Programming}},
year = {2009}
}