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The automatic estimating method of the in-degree of nodes in associated semantic network oriented to big data

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Abstract

Association Link Network (ALN) can organize massive news data to support many intelligent Web applications. The degree estimating can facilitate the rapid positioning of Web resources in ALN. In our prior work, we have well studied the degree estimating of out-degree of nodes in ALN. In this paper, we proposed an automatic estimating method of the in-degree of nodes in ALN to further reduce the searching scope for the rapid positioning. First, we explore the main factors of forming the in-degree of any one node from semantic feature view by qualitative analysis. Then, based on the result of qualitative analysis, we propose the model for estimating the in-degree of any one node in ALN, including the method framework, the first automatic estimating method and its further optimization method. Experimental results show that the proposed estimating method as well as the optimization method have a high precision.

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Acknowledgments

This work was supported by the Natural Science Foundation of Anhui Province Universities (No. KJ2015A111, KJ2011Z098), in part by the National Science and Technology Major Project under Grant 2013ZX01033002-003, in part by the National Science Foundation of China under Grant 61300202, and in part by the Science Foundation of Shanghai under Grant 13ZR1452900.

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Authors and Affiliations

  1. Anhui University of Science & Technology, Huainan, China

    Shunxiang Zhang, Xiaobo Yin & Congna He

  2. Changzhou Institute of Light Industry Technology, Changzhou, China

    Congna He

  3. School of Computer Engineering and Science, Shanghai University, Shanghai, China

    Xiaobo Yin

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  1. Shunxiang Zhang
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  2. Xiaobo Yin
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  3. Congna He
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Correspondence to Xiaobo Yin.

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Zhang, S., Yin, X. & He, C. The automatic estimating method of the in-degree of nodes in associated semantic network oriented to big data. Cluster Comput 19, 1895–1905 (2016). https://doi.org/10.1007/s10586-016-0658-6

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  • DOI: https://doi.org/10.1007/s10586-016-0658-6

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