Knowledge and Information Systems

, Volume 37, Issue 1, pp 61–81 | Cite as

Geographic knowledge extraction and semantic similarity in OpenStreetMap

  • Andrea Ballatore
  • Michela Bertolotto
  • David C. Wilson
Regular Paper

Abstract

In recent years, a web phenomenon known as Volunteered Geographic Information (VGI) has produced large crowdsourced geographic data sets. OpenStreetMap (OSM), the leading VGI project, aims at building an open-content world map through user contributions. OSM semantics consists of a set of properties (called ‘tags’) describing geographic classes, whose usage is defined by project contributors on a dedicated Wiki website. Because of its simple and open semantic structure, the OSM approach often results in noisy and ambiguous data, limiting its usability for analysis in information retrieval, recommender systems and data mining. Devising a mechanism for computing the semantic similarity of the OSM geographic classes can help alleviate this semantic gap. The contribution of this paper is twofold. It consists of (1) the development of the OSM Semantic Network by means of a web crawler tailored to the OSM Wiki website; this semantic network can be used to compute semantic similarity through co-citation measures, providing a novel semantic tool for OSM and GIS communities; (2) a study of the cognitive plausibility (i.e. the ability to replicate human judgement) of co-citation algorithms when applied to the computation of semantic similarity of geographic concepts. Empirical evidence supports the usage of co-citation algorithms—SimRank showing the highest plausibility—to compute concept similarity in a crowdsourced semantic network.

Keywords

Semantic similarity OpenStreetMap Volunteered Geographic Information OSM Semantic Network SimRank P-Rank Co-citation Crowdsourcing 

Notes

Acknowledgments

The research presented in this paper was funded by a Strategic Research Cluster grant (07/SRC/I1168) by Science Foundation Ireland under the National Development Plan. The authors gratefully acknowledge this support. They also wish to thank the anonymous reviewers for their valuable suggestions, and Prof. Leslie Daly (UCD School of Public Health, Physiotherapy & Population Science) for his insightful comments on statistical meta-analysis.

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Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • Andrea Ballatore
    • 1
  • Michela Bertolotto
    • 1
  • David C. Wilson
    • 2
  1. 1.School of Computer Science and InformaticsUniversity College DublinDublin 4Ireland
  2. 2.Department of Software and Information SystemsUniversity of North CarolinaCharlotteUSA

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