Extracting Geospatial Entities from Wikipedia

This paper addresses the challenge of extracting geospatial data from the article text of the English Wikipedia. In the first phase of our work, we create a training corpus and select a set of word-based features to train a Support Vector Machine (SVM) for the task of geospatial named entity recognition. We target for testing a corpus of Wikipedia articles about battles and wars, as these have a high incidence of geospatial content. The SVM recognizes place names in the corpus with a very high recall, close to 100\%, with an acceptable precision. The set of geospatial NEs is then fed into a geocoding and resolution process, whose goal is to determine the correct coordinates for each place name. As many place names are ambiguous, and do not immediately geocode to a single location, we present a data structure and algorithm to resolve ambiguity based on sentence and article context, so the correct coordinates can be selected. We achieve an f-measure of 82\%, and create a set of geospatial entities for each article, combining the place names, spatial locations, and an assumed point geometry. These entities can enable geospatial search on and geovisualization of Wikipedia.