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

被引:1
|
作者
Zhang, Shunxiang [1 ]
Yin, Xiaobo [1 ,3 ]
He, Congna [1 ,2 ]
机构
[1] Anhui Univ Sci & Technol, Huainan, Peoples R China
[2] Changzhou Inst Light Ind Technol, Changzhou, Peoples R China
[3] Shanghai Univ, Sch Comp Engn & Sci, Shanghai, Peoples R China
来源
Cluster Computing-The Journal of Networks Software Tools and Applications | 2016年 / 19卷 / 04期
基金
中国国家自然科学基金;
关键词
Association Link Network; In-degree; Automatic estimating; Optimization method; Cluster computing; WEB SEARCH ENGINES; COMPLEX NETWORKS; KNOWLEDGE FLOW; SMALL-WORLD; REPRESENTATION; DYNAMICS; INTERNET; MODEL;
D O I
10.1007/s10586-016-0658-6
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:1895 / 1905
页数:11
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