Agricultural Ontology Based Feature Optimization for Agricultural Text Clustering

• Published in: Journal of Integrative Agriculture Vol. 11; no. 5; pp. 752 - 759
• Main Authors: SU, Ya-ru, WANG, Ru-jing, CHEN, Peng, WEI, Yuan-yuan, LI, Chuan-xi, HU, Yi-min
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2012; Science Press; School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, P.R.China; Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, P.R.China; Elsevier
• Subjects: agricultural ontology; agricultural ontology feature optimization agricultural text clustering; agricultural text clustering; feature optimization; 农业; 向量空间模型; 文本聚类; 本体论; 特征优化; 特征频率; 语义信息; 重量计算; agricultural ontology; agricultural text clustering; feature optimization
• ISSN: 2095-3119; 2352-3425
• DOI: 10.1016/S2095-3119(12)60064-1

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: the curse 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.