Department of Cognitive Sciences, University of California, IrvineDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology.
Cogn Sci. 2005 Jan 2;29(1):41-78. doi: 10.1207/s15516709cog2901_3.
We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of learning history variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks.
我们呈现了 3 种语义网络（词汇联想、WordNet 和 Roget's Thesaurus）的大规模结构的统计分析。我们表明它们具有小世界结构的特征，表现为连接稀疏、单词之间的平均路径长度短、局部聚类强。此外，连接数量的分布遵循幂律，表明连接具有无标度模式，大多数节点的连接相对较少，通过少数连接较多的枢纽连接在一起。这些规律性也在其他一些复杂的自然网络中被发现，如万维网，但它们与基于继承层次结构、任意结构网络或高维向量空间的许多传统语义组织模型不一致。我们提出这些结构反映了语义网络增长的机制。我们描述了一种简单的语义增长模型，其中每个新单词或概念通过区分现有节点的连接模式与现有网络连接。该模型生成了适当的小世界统计和幂律连接分布，并且还为学习历史变量（习得年龄、使用频率）对语义处理任务中的行为表现的影响提供了一种可能的机械基础。
