Taxonomy learning using compound similarity measure

被引:6
|
作者
Neshati, Mahmood [1 ]
Alijamaat, Ali [1 ]
Abolhassani, Hassan [1 ]
Rahimi, Afshin [1 ]
Hoseini, Mehdi [1 ]
机构
[1] Sharif Univ Technol, Dept Comp Engn, Web Intelligence Res Lab, Tehran, Iran
来源
PROCEEDINGS OF THE IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE: WI 2007 | 2007年
关键词
D O I
10.1109/WI.2007.135
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Taxonomy learning is one of the major steps in ontology learning process. Manual construction of taxonomies is a time-consuming and cumbersome task. Recently many researchers have focused on automatic taxonomy learning, but still quality of generated taxonomies is not satisfactory. In this paper we have proposed a new compound similarity measure. This measure is based on both knowledge poor and knowledge rich approaches to find word similarity. We also used Machine Learning Technique (Neural Network model) for combination of several similarity methods. We have compared our method with simple syntactic similarity measure. Our measure considerably improves the precision and recall of automatic generated taxonomies.
引用
收藏
页码:487 / 490
页数:4
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