[1]兰富菊,赵志弘,韩永国.基于领域本体的主观题自动评阅算法的研究[J].计算机技术与发展,2014,24(06):166-169.
　LAN Fu-ju,ZHAO Zhi-hong,HAN Yong-guo.Research on Subjective Machine Marking Algorithm Based on Domain Ontology[J].,2014,24(06):166-169.
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基于领域本体的主观题自动评阅算法的研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年06期
页码:
166-169
栏目:
应用开发研究
出版日期:
2014-06-30

文章信息/Info

Title:
Research on Subjective Machine Marking Algorithm Based on Domain Ontology
文章编号:
1673-629X（2014）06-0166-04
作者:
兰富菊; 赵志弘; 韩永国
西南科技大学 计算机科学与技术学院
Author(s):
LAN Fu-ju; ZHAO Zhi-hong; HAN Yong-guo
关键词:
领域本体; 相似度矩阵; 权值; 语义关系; 向量空间模型
Keywords:
domain ontology; similarity matrices; weighting; semantic relation; VSM
分类号:
TP391
文献标志码:
A
摘要:
针对VSM不能揭示文档中特征词间的潜在语义关系，相似度计算准确性较低的问题，结合本体模型的结构特点，从语义重合度、语义距离以及本体结构等因素综合考虑概念间的相似度计算，提出了一种基于领域本体的文档向量空间模型。该模型通过构建概念间的语义相似度矩阵对特征词权值进行调整，建立包含语义关系的标准(学生)答案的向量空间模型，并用“VSM模型+余弦值”算法评估学生答案和标准答案的相似度。实验表明，与传统方法相比，该方法提高了评测效果及准确率。
Abstract:
In view of the problems that VSM couldn't reveal the latent semantic relations between the key words in a document,and have a low accuracy in document similarity calculation,combined the structural feature of ontology model,considering the concept similarity from the semantic contact ratio,semantic distance and the ontology structure,a document vector space model based on domain ontology is proposed. The model adjusts the weight of feature words by building concept semantic similarity matrix,constructing the standard answer and the student answer VSM contains semantic relation, and making use of the “VSM+cosine” algorithm to assess similarity of the student answer and the standard answer. Compared with the traditional method,experiments show that this method improves the scoring results and accuracy.

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更新日期/Last Update: 1900-01-01