Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets
Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets
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DOI：
10.3390/ijgi7030117
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发表时间：
2018-03
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通讯作者：
T. Novack;Robin Peters;A. Zipf
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文献类型：
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作者：
T. Novack;Robin Peters;A. Zipf
一些地理空间研究和应用程序需要从兴趣点（POI）的综合语义信息。然而，这些信息经常分散在不同的协作地图绘制平台上。令人惊讶的是，仍然有一个研究空白的合并POI从这种类型的地理数据集。在本文中，我们专注于POI数据合并的匹配方面，提出了两种匹配策略的基础上，图的节点表示POI和边缘表示匹配的可能性。我们演示了该图如何用于（1）动态定义我们考虑的不同POI相似性度量的权重;（2）解决POI在其他数据集上没有对应POI时应保持不匹配的问题，以及（3）从同一数据集中的同一位置检测多个POI，并将这些POI与其他数据集中的对应POI联合匹配。我们提出的策略不需要收集训练样本或广泛的参数调整。将它们与一种“天真”（尽管普遍应用）的匹配方法进行统计比较，该方法考虑了从OpenStreetMap和来自伦敦（英国）市的Foursquare收集的兴趣点。在我们的实验中，我们在匹配过程中顺序地包括了我们的每一个方法建议，与以前的结果相比，它们中的每一个都导致了准确性的提高。我们的最佳匹配结果达到了91%的整体准确率，比基线方法的准确率高出10%以上。
Several geospatial studies and applications require comprehensive semantic information from points-of-interest (POIs). However, this information is frequently dispersed across different collaborative mapping platforms. Surprisingly, there is still a research gap on the conflation of POIs from this type of geo-dataset. In this paper, we focus on the matching aspect of POI data conflation by proposing two matching strategies based on a graph whose nodes represent POIs and edges represent matching possibilities. We demonstrate how the graph is used for (1) dynamically defining the weights of the different POI similarity measures we consider; (2) tackling the issue that POIs should be left unmatched when they do not have a corresponding POI on the other dataset and (3) detecting multiple POIs from the same place in the same dataset and jointly matching these to the corresponding POI(s) from the other dataset. The strategies we propose do not require the collection of training samples or extensive parameter tuning. They were statistically compared with a “naive”, though commonly applied, matching approach considering POIs collected from OpenStreetMap and Foursquare from the city of London (England). In our experiments, we sequentially included each of our methodological suggestions in the matching procedure and each of them led to an increase in the accuracy in comparison to the previous results. Our best matching result achieved an overall accuracy of 91%, which is more than 10% higher than the accuracy achieved by the baseline method.