Classification of RSS feed news items using ontology

Shikha Agarwal, A. Singhal, Punam Bedi
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引用次数: 21

Abstract

Explosive growth of data on the web demand techniques, which would enable the user to access desired information. In Information retrieval Document Classification is prerequisite. In practice many classification techniques were and are in use. Term Frequency-Inverse Document Frequency (TF-IDF) is an approach which represents documents based on the frequency of terms in documents. Limitation of this approach is high dimensionality of data. Moreover it does not consider the relations among the terms, resulting in less precise and noisy end result. In our approach we are using weighted Concept Frequency-Inverse Document Frequency (CF-IDF) with background knowledge of domain Ontology, for classification of RSS feed News Items. Metadata information of news items has been used to assign weight to the identified concepts. No trained classifiers are required as Ontology itself acts as a classifier. We have designed ontology based on news industry standards. This classification approach considers relations among the concepts and properties. It results in reduction of noise in final output. It considers only the key concepts of a domain for classification instead of all the terms, which curbs the problem of dimensionality. Evaluation of experimental results reveals that proposed approach gives better classification results.
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使用本体对RSS提要新闻项进行分类
网络上数据的爆炸式增长需要技术,这将使用户能够访问所需的信息。文献分类是信息检索的前提。在实践中,许多分类技术过去和现在都在使用。术语频率-逆文档频率(TF-IDF)是一种基于术语在文档中出现的频率来表示文档的方法。这种方法的局限性是数据的高维性。此外,它没有考虑项之间的关系,导致最终结果精度较低且有噪声。在我们的方法中,我们使用具有领域本体背景知识的加权概念频率-逆文档频率(CF-IDF)来对RSS提要新闻条目进行分类。使用新闻条目的元数据信息为已识别的概念分配权重。不需要经过训练的分类器,因为本体本身就是一个分类器。我们基于新闻行业标准设计了本体。这种分类方法考虑了概念和属性之间的关系。它可以降低最终输出中的噪声。它只考虑一个领域的关键概念进行分类,而不是考虑所有的术语,从而抑制了维度问题。实验结果表明,该方法具有较好的分类效果。
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