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(英文)

作　　者：唐璐[1] 张永光[1] 付雪[1] 

机构地区：[1]中国科学院数学与系统科学研究院

出　　处：《Journal of Southeast University(English Edition)》2006年第3期413-417,共5页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.60275016).

摘　　要：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.

关 键 词：semantic networks complex networks SMALL-WORLD SCALE-FREE hierarchical organization 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]