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A model for estimating the out‐degree of nodes in associated semantic network from semantic feature view
Author(s) -
Zhang Shunxiang,
Wang Yin,
Liu Weidong,
Yin Xiaobo
Publication year - 2016
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3819
Subject(s) - computer science , degree (music) , node (physics) , feature (linguistics) , data mining , scope (computer science) , semantic web , semantics (computer science) , semantic feature , information retrieval , artificial intelligence , linguistics , philosophy , physics , structural engineering , acoustics , engineering , programming language
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.