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Knowledge and Information Systems

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01-10-2013 | Regular Paper

Geographic knowledge extraction and semantic similarity in OpenStreetMap

Authors: Andrea Ballatore, Michela Bertolotto, David C. Wilson

Published in: Knowledge and Information Systems | Issue 1/2013

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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.

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This usage of the term ‘tag’ is highly unusual: in the Web 2.0, tags are generally unstructured text labels used as meta-data [22]. However, to be consistent with the OSM terminology, we will refer to the OSM properties as ‘tags’ in the rest of this paper.

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Pre-computed similarity scores for the entire OSM Semantic Network are available at http://spatial.ucd.ie/osn/similarities (acc. August 10, 2012).

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http://www.w3.org/RDF (acc. August 10, 2012).

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Title: Geographic knowledge extraction and semantic similarity in OpenStreetMap
Authors: Andrea Ballatore
Michela Bertolotto
David C. Wilson
Publication date: 01-10-2013
Publisher: Springer London
Published in: Knowledge and Information Systems / Issue 1/2013
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-012-0571-0

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