Semantic Similarity Algorithm Based on Generalized Regression Neural Network

DOI

10.2991/icismme-15.2015.286How to use a DOI?

Keywords

semantic similarity; GRNN; semantic web; neural network; cross-validation.

Abstract

Based on the intensives study of semantic similarity algorithms and artificial neural networks knowledge, a generalized regression neural network semantic similarity algorithm is proposed. Training samples are obtained by extracting the principal component of semantic similarity influence factors; the desired spread factor and best training sample sets are gotten by cross- validation and recursive optimization; a generalized regression neural network is established with these supports. Experiment comparison and analysis verify that, the result of semantic similarity algorithm based on generalized regression neural network is more accurate than that of existing algorithms.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Rui Cao
AU  - Lingda Wu
AU  - Rui Wang
AU  - Chao Yang
PY  - 2015/07
DA  - 2015/07
TI  - Semantic Similarity Algorithm Based on Generalized Regression Neural Network
BT  - Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
PB  - Atlantis Press
SP  - 1332
EP  - 1335
SN  - 1951-6851
UR  - https://doi.org/10.2991/icismme-15.2015.286
DO  - 10.2991/icismme-15.2015.286
ID  - Cao2015/07
ER  -