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Learning to Classify Spatiotextual Entities in Maps

  • Giorgos GiannopoulosEmail author
  • Nikos Karagiannakis
  • Dimitrios Skoutas
  • Spiros Athanasiou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)

Abstract

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.

Keywords

Support Vector Machine Support Vector Machine Model Cosine Similarity Training Feature Volunteer Geographic Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was partially supported by EU projects GeoKnow (GA no. 318159) and City.Risks (H2020-FCT-2014-653747).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Giorgos Giannopoulos
    • 1
    Email author
  • Nikos Karagiannakis
    • 1
  • Dimitrios Skoutas
    • 1
  • Spiros Athanasiou
    • 1
  1. 1.IMIS Institute, “Athena” Research CenterAthensGreece

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