Similarity measure of test questions based on ontology and VSM

被引：1

作者：

Yu, Jing ^{[1
]}

Li, Dongmei ^{[1
]}

Hou, Jiajia ^{[1
]}

Liu, Ying ^{[1
]}

Yang, Zhaoying ^{[1
]}

机构：

[1] School of Information Science and Technology, Beijing Forestry University, Beijing, China

来源：

Open Automation and Control Systems Journal | 2014年 / 6卷 / 01期

关键词：

Vector spaces - Semantics - Testing - Trees (mathematics);

D O I：

10.2174/1874444301406010262

中图分类号：

学科分类号：

摘要：

Vector space model (VSM) is a common method for measuring test questions similarity in massive item bank system. VSM is limited in accurately representing the knowledge relationship and the potential semantic relations of different characteristic words, hence this paper proposes a method of test questions similarity measure called OVSMTQSM which combines domain ontology and VSM. OVSM-TQSM can reveal the intrinsic relationship among words by using the constructed domain ontology which integrates with the tree structure and the graphics structure. Incorporated with eigenvectors and the weight of words in VSM, OVSM-TQSM calculates the similarity of test questions. A large number of experimental results demonstrate that the novel approach is feasible and effective. Compared with the traditional method based on VSM, OVSM-TQSM has the advantages of higher accuracy and little unnecessary laborious pre-processing. © Yu et al.; Licensee Bentham Open.

引用

页码：262 / 267