Agricultural Ontology Based Feature Optimization for Agricultural Text Clustering  

作　　者：SU Ya-ru WANG Ru-jing CHEN Peng WEI Yuan-yuan LI Chuan-xi HU Yi-min 

机构地区：[1]Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, P.R.China [2]School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, P.R.China

出　　处：《Journal of Integrative Agriculture》2012年第5期752-759,共8页农业科学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (60774096);the National HighTech R&D Program of China (2008BAK49B05)

摘　　要：Feature optimization is important to agricultural text mining. Usually, the vector space model is used to represent text documents. However, this basic approach still suffers from two drawbacks： thecurse of dimension and the lack of semantic information. In this paper, a novel ontology-based feature optimization method for agricultural text was proposed. First, terms of vector space model were mapped into concepts of agricultural ontology, which concept frequency weights are computed statistically by term frequency weights; second, weights of concept similarity were assigned to the concept features according to the structure of the agricultural ontology. By combining feature frequency weights and feature similarity weights based on the agricultural ontology, the dimensionality of feature space can be reduced drastically. Moreover, the semantic information can be incorporated into this method. The results showed that this method yields a significant improvement on agricultural text clustering by the feature optimization.Feature optimization is important to agricultural text mining. Usually, the vector space model is used to represent text documents. However, this basic approach still suffers from two drawbacks： thecurse of dimension and the lack of semantic information. In this paper, a novel ontology-based feature optimization method for agricultural text was proposed. First, terms of vector space model were mapped into concepts of agricultural ontology, which concept frequency weights are computed statistically by term frequency weights; second, weights of concept similarity were assigned to the concept features according to the structure of the agricultural ontology. By combining feature frequency weights and feature similarity weights based on the agricultural ontology, the dimensionality of feature space can be reduced drastically. Moreover, the semantic information can be incorporated into this method. The results showed that this method yields a significant improvement on agricultural text clustering by the feature optimization.

关 键 词：agricultural ontology feature optimization agricultural text clustering 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP311.13[自动化与计算机技术—计算机科学与技术]