[1] Tang Lu, , Zhang Yongguang, et al. Structures of semantic networks:how do we learn semantic knowledge [J]. Journal of Southeast University (English Edition), 2006, 22 (3): 413-417. [doi:10.3969/j.issn.1003-7985.2006.03.026]

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Structures of semantic networks:how do we learn semantic knowledge()

语义网络的结构:我们怎样学习语义知识

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

22

Issue:

2006 3

Page:

413-417

Research Field:

Automation

Publishing date:

2006-09-30

Info

Title:

Structures of semantic networks:how do we learn semantic knowledge

语义网络的结构:我们怎样学习语义知识

Author(s):

Tang Lu^{1, 2, 4}, Zhang Yongguang¹, Fu Xue^{1, 2, 3}

¹Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China
²Graduate School, Chinese Academy of Sciences, Beijing 100080, China
³School of Banking and Public Finance, Jiangxi University of Finance, Nanchang 330013, China
⁴Bussiness School, University of Shanghai for Science and Technology, Shanghai 200093, China

唐璐^{1, 2, 4}, 张永光¹, 付雪^{1, 2, 3}

¹中国科学院数学与系统科学研究院, 北京 100080; ²中国科学院研究生院, 北京 100080; ³江西财经大学公共管理学院, 南昌 330013; ⁴上海理工大学管理学院, 上海 200093

Keywords:

semantic networks;  complex networks;  small-world;  scale-free;  hierarchical organization

语义网络;  复杂网络;  小世界;  无尺度;  层次结构

PACS:

TP182

DOI:

10.3969/j.issn.1003-7985.2006.03.026

Abstract:

Global semantic structures of two large semantic networks, HowNet and WordNet, are analyzed.It is found that they are both complex networks with features of small-world and scale-free, but with special properties.Exponents of power law degree distribution of these two networks are between 1.0 and 2.0, different from most scale-free networks which have exponents near 3.0.Coefficients of degree correlation are lower than 0, similar to biological networks.The BA(Barabasi-Albert)model and other similar models cannot explain their dynamics.Relations between clustering coefficient and node degree obey scaling law, which suggests that there exist self-similar hierarchical structures in networks.The results suggest that structures of semantic networks are influenced by the ways we learn semantic knowledge such as aggregation and metaphor.

分析了2个大型语义网络HowNet和WordNet的全局意义结构.发现两者都是具有小世界和无尺度特征的复杂网络, 但具有一些独特的属性.两者连接度分布的幂律指数介于1.0和2.0之间, 而不是像许多常见的无尺度网络一样接近于3.0.连接度相关系数都小于0, 与生物性网络相似.BA模型以及与其相似的一些模型不能对其动力学加以解释.节点连接度与其聚集度指数之间遵循标度律, 表明网络中可能存在自相似的层次结构.认为人类学习语义知识的几种主要方式如聚合与隐喻等影响了语义网络的这些结构特征.

References:

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Memo

Memo:

Biographies: Tang Lu(1977—), male, graduate;Zhang Yongguang(corresponding author), male, professor, yzhang@iss.ac.cn.

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