In this paper, we present an approach for automatically recommending categories for spatiotextual entities, based on already existing annotated entities. Our goal is to facilitate the annotation process in crowdsourcing map initiatives such as OpenStreetMap, so that more accurate annotations are produced for the newly created spatial entities, while at the same time increasing the reuse of already existing tags. We define and construct a set of training features to represent the attributes of the spatiotextual entities and to capture their relation with the categories they are annotated with. These features include spatial, textual and semantic properties of the entities. We evaluate four different approaches, namely SVM, kNN, clustering+SVM and clustering+kNN, on several combinations of the defined training features and we examine which configurations of the algorithms achieve the best results. The presented work is deployed in OSMRec, a plugin for the JOSM tool that is commonly used for editing content in OpenStreetMap.
['Giorgos Giannopoulos', 'Nikos Karagiannakis', 'Dimitrios Skoutas', 'Spiros Athanasiou']
Learning to Classify Spatiotextual Entities in Maps