论文标题
从主题网络到分布式认知图:志愿地理信息领域的Zipfian主题宇宙
From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information
论文作者
论文摘要
附近的地方(例如城市)是否用相关词描述?在本文中,我们将这个研究问题在地理信息的词汇编码领域转移到互文性的水平上。为此,我们探索了志愿地理信息(VGI),以模拟借助所谓的主题网络来解决城市或地区级别的地点的文本。这样做是为了研究语言如何编码和网络有关文本的关于性质级别的地理信息。我们的假设是,在地点的网络主题是相似的 - 无论其距离和基本作者社区如何。为了调查这一点,我们介绍了多重主题网络(MTN),我们自动从语言多层网络(LMN)自动衍生作为一种新型模型,尤其是文本语料库中的主题网络。我们的研究表明,主题宇宙的Zipfian组织,地理位置(尤其是城市)位于在线交流中。我们在认知图的背景下解释了这一发现,这是我们通过所谓的主题图扩展的概念。根据我们对这一发现的解释,主题地图作为认知图的一部分的组织是由作者产生可共享内容的倾向,从而确保了基础媒体的持续存在。我们以特殊Wikis和Wikipedia提取物的示例来检验我们的假设。通过这种方式,我们得出结论:无论是否彼此靠近,位置都位于跨越主题宇宙中类似子网的相邻地方。
Are nearby places (e.g. cities) described by related words? In this article we transfer this research question in the field of lexical encoding of geographic information onto the level of intertextuality. To this end, we explore Volunteered Geographic Information (VGI) to model texts addressing places at the level of cities or regions with the help of so-called topic networks. This is done to examine how language encodes and networks geographic information on the aboutness level of texts. Our hypothesis is that the networked thematizations of places are similar - regardless of their distances and the underlying communities of authors. To investigate this we introduce Multiplex Topic Networks (MTN), which we automatically derive from Linguistic Multilayer Networks (LMN) as a novel model, especially of thematic networking in text corpora. Our study shows a Zipfian organization of the thematic universe in which geographical places (especially cities) are located in online communication. We interpret this finding in the context of cognitive maps, a notion which we extend by so-called thematic maps. According to our interpretation of this finding, the organization of thematic maps as part of cognitive maps results from a tendency of authors to generate shareable content that ensures the continued existence of the underlying media. We test our hypothesis by example of special wikis and extracts of Wikipedia. In this way we come to the conclusion: Places, whether close to each other or not, are located in neighboring places that span similar subnetworks in the topic universe.