Online social networks convey rich information about geospatial facets of reality. However in most cases, geographic information is not explicit and structured, thus preventing its exploitation in real-time applications. We address this limitation by introducing a novel geoparsing and geotagging technique called Geo-Semantic-Parsing (GSP). GSP identifies location references in free text and extracts the corresponding geographic coordinates. To reach this goal, we employ a semantic annotator to identify relevant portions of the input text and to link them to the corresponding entity in a knowledge graph. Then, we devise and experiment with several efficient strategies for traversing the knowledge graph, thus expanding the available set of information for the geoparsing task. Finally, we exploit all available information for learning a regression model that selects the best entity with which to geotag the input text. We evaluate GSP on a well-known reference dataset including almost 10 k event-related tweets, achieving F1 = 0.66. We extensively compare our results with those of 2 baselines and 3 state-of-the-art geoparsing techniques, achieving the best performance. On the same dataset, competitors obtain F1 ≤ 0.55. We conclude by providing in-depth analyses of our results, showing that the overall superior performance of GSP is mainly due to a large improvement in recall, with respect to existing techniques.

在线社交网络传达有关现实地理空间方面的丰富信息。但是，在大多数情况下，地理信息不是显式的和结构化的，因此阻止了其在实时应用中的利用。我们通过引入一种称为地理语义解析（GSP）的新型地理解析和地理标记技术来解决此限制。普惠制以自由文本标识位置参考，并提取相应的地理坐标。为了实现此目标，我们使用语义注释器来识别输入文本的相关部分，并将它们链接到知识图中的相应实体。然后，我们设计并试验了几种遍历知识图的有效策略，从而扩展了地理解析任务的可用信息集。最后，我们利用所有可用信息来学习回归模型，该模型选择用来对输入文本进行地理标记的最佳实体。我们在著名的参考数据集上评估了GSP，其中包括将近10  k与事件相关的推文，从而实现了F1 = 0.66。我们将结果与2种基线和3种最先进的地理解析技术进行了广泛的比较，以实现最佳性能。在同一数据集上，竞争对手获得F 1≤0.55。我们通过对结果进行深入分析来得出结论，表明GSP总体上的卓越性能主要是由于相对于现有技术，召回率有了很大的提高。