A model for estimating the out‐degree of nodes in associated semantic network from semantic feature view. (22nd March 2016)

• Record Type: Journal Article
• Authors: Zhang, Shunxiang; Wang, Yin; Liu, Weidong; Yin, Xiaobo
• Other Names: Bellatreche Ladjel guestEditor.; Mohania Mukesh guestEditor.; Luo Xiangfeng guestEditor.; Liu Yunhuai guestEditor.; Li Qing guestEditor.
• Is Part Of: Concurrency and computation. Volume 28:Number 15(2016)
• Page Start: 4177
• Page End: 4193
• Publication Date: 2016-03-22
• Subjects: Association Link Network -- out‐degree -- estimating -- model framework -- optimization method; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3819
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Association Link Network (ALN) can organize massive news data to support many intelligent Web applications. The degree estimating of nodes in ALN, including out‐degree and in‐degree, is an important and significant research. It can provide effective support for some applications such as the control of network structure, the rapid positioning of Web resources in ALN. This paper proposes a model for estimating the out‐degree of any one node in ALN from semantic feature view, which can greatly reduce the searching scope for the rapid positioning of Web resources stored in large‐scale database. First, we explore the main factors of forming the out‐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 out‐degree of any one node in ALN, including the model framework, the first estimating theory and its further optimization method. Experimental results show that the proposed estimating model as well as the optimization method have a high precision. Copyright © 2016 John Wiley & Sons, Ltd.