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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Extracting Low-density and Valuable Association Semantic Link from Domain News
verfasst von : Shunxiang Zhang, Pingyi Zhou, Zekun Liu, Zheng Xu
Erschienen in: Future Information Technology
Verlag: Springer Berlin Heidelberg
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Association Semantic Link (ASL) can provide theoretical support for many web intelligent activities. However, when we extract the keywords-level Association Semantic Link (k-ASL), a kind of low-density and valuable k-ASL is easy to be discarded because of its sparse distribution. To solve this problem, an extracting approach is proposed to mine this type of low-density and valuable k-ASL. First, the time validity for three types of k-ASL is analyzed to clear and define their semantic characteristic. Second, based on the analysis of the time validity for k-ASL, the existing problems for mining low-density and valuable k-ASL is described. At last, we present the approach for extracting this low-density and valuable k-ASL. Experimental results verify the correctness of the proposed approach.