Semantic similarity analysis of user-generated content for theme-based route planning
Semantic similarity analysis of user-generated content for theme-based route planning
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DOI：
10.1080/17489725.2013.804214
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发表时间：
2013-12
影响因子：
2.3
通讯作者：
Karsten Pippig;D. Burghardt;Nikolas Prechtel
中科院分区：
文献类型：
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作者：
Karsten Pippig;D. Burghardt;Nikolas Prechtel
近年来，已经公布了几种旅游规划的技术方法，这些方法将受欢迎的景点和感兴趣的路线联系起来，或者提供与特定主题相关的位置。因此，兴趣点是根据用户生成的网络内容找到和评估的。然而，据作者所知，没有一种方法可以真正实现个性化的主题路线规划。个性化意味着，用户灵活地定义起点和目的地，并接收优化的路线，这将引导他通过城市景观/景观，其中最有趣的特征位于沿着。我们介绍了两种方法来找到这样一个基于用户生成的内容的个人主题路线。这两种方法的基础是确定所选维基百科概念（例如特定的建筑风格）和其他地理参考维基百科概念（例如建筑物）之间的语义相似性。第一种方法被称为连续体方法：它使用语义相似性度量以及网络中与主题相关的地理标记照片的密度分布，以创建连续的“吸引力表面”。这种概念上的连续体可以与网络要素的静态几何长度一起形成将阻抗值分配给导航图的基础。第二种方法被称为点序列法：它将主题路线建模为旅行商问题的特定版本。通过从起点到终点顺序地将访问点添加到导航图来组成路线。优先级是从排序的语义相似性值中得出的。在用户调查的基础上对取得的成果进行了比较和评价。
Several technical approaches to a touristic tour planning, which connect popular points and routes of interest or provide locations related to specific themes, have been published in recent years. Hereby, points of interest are found and evaluated on the basis of user-generated web content. However, no approach exists to the author's knowledge, which allows truly individual theme route planning. Individual means, that a user flexibly defines start point and destination and receives an optimised route, which will guide him through a townscape/landscape with most interesting features being situated along the proposed way. We introduce two methods to find such an individual theme route based on user-generated content. The basis for both methods is the determination of semantic similarity between a selected Wikipedia concept (e.g. a specific architectural style) and other geo-referenced Wikipedia concepts (e.g. a building). The first method has been termed the continuum method: it uses semantic similarity measures together with a density distribution from theme-related, geo-tagged photos in the web, in order to create a continuous ‘surface of attractiveness’. Such a conceptual continuum can – together with the static geometric length of network features – form the basis of an assignment of impedance values to a navigation graph. The second method has been termed the spot sequence method: it models the theme route as a specific version of the travelling salesman problem. A route is composed by sequentially adding visit points to a navigation graph from the start to the end point. Priorities are derived from the ranked semantic similarity values. The achieved results have been compared and evaluated on a basis of a user survey.