Reza Mohamadi Bahram Abadi, M. H. Yektaie, M. Abbasi
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摘要

考虑到万维网上信息的日益发展,用户发现很难获得他/她所需要的文件。本文的目的是提出一种利用统计技术使用户搜索更加系统和有限的方法。为此,我们将通过多个线性回归模型提出一个公式,以便对词汇对象和本体之间的关系进行建模。然后,为了在样本文档上陈述想法,我们计算该文档中的视图值,这些值符合本体中的词法对象,然后我们将形成文档向量。通过多元线性回归得到优化后的公式中的文档值,我们可以预测文档向量与本体向量之间的夹角程度。角度越接近于零,表示文档与本体的关系越多。实验结果表明,该方法对该角度的识别准确率为100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Angle Prediction between Document Vector and Ontology Vector, Using Multiple Linear Regressions
Considering the growing development of information at World Wide Web, the users find it difficult to have access to the documents s/he requires. The purpose of this paper is to present a method for making the search by user more systematic and limited using some statistical techniques. For this purpose, we will present a formula by multiple linear regression models in order to model the relation between lexical objects and ontology. Then for stating ideas on a sample document, we count view values in that document, which are conforming to lexical objects in ontology, and next we will form the document vector. With having optimized document value in the formula out of multiple linear regressions, we can predict the degree of angle between the document vector and ontology vector. The closer the angle to zero, the more relation the document has ontology. Experimental Results show the recommended method would be able to distinguish 100% accuracy of this angle.
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