Geographic Knowledge Extraction and Semantic Similarity in OpenStreetMap

DC FieldValueLanguage
dc.contributor.authorBallatore, Andrea
dc.contributor.authorBertolotto, Michela
dc.contributor.authorWilson, David C.
dc.date.accessioned2012-12-19T15:50:44Z
dc.date.available2013-10-01T03:00:08Z
dc.date.copyright2012 Springer-Verlag Londonen
dc.date.issued2012-10
dc.identifier.citationKnowledge and Information Systemsen
dc.identifier.issn0219-1377
dc.identifier.urihttp://hdl.handle.net/10197/3973
dc.description.abstractIn 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.en
dc.description.sponsorshipScience Foundation Irelanden
dc.language.isoenen
dc.publisherSpringeren
dc.subjectSemantic similarityen
dc.subjectOpenStreetMapen
dc.subjectVolunteered Geographic Informationen
dc.subjectOSM semantic networken
dc.subjectSimRanken
dc.subjectP-Ranken
dc.subjectCo-citationen
dc.subjectCrowdsourcingen
dc.titleGeographic Knowledge Extraction and Semantic Similarity in OpenStreetMapen
dc.typeJournal Articleen
dc.internal.authorcontactotherandrea.ballatore@ucd.ie
dc.internal.availabilityFull text availableen
dc.statusPeer revieweden
dc.identifier.volume37en
dc.identifier.issue1
dc.identifier.startpage61
dc.identifier.endpage81
dc.identifier.doi10.1007/s10115-012-0571-0-
dc.neeo.contributorBallatore|Andrea|aut|-
dc.neeo.contributorBertolotto|Michela|aut|-
dc.neeo.contributorWilson|David C.|aut|-
dc.description.othersponsorshipStrategicResearch Cluster grant (07/SRC/I1168) by Science Foundation Ireland under the National Development Planen
dc.description.adminDG 30/10/2012en
dc.description.adminCitation info will need to be updated once the embargo ends in October 2013en
dc.internal.rmsid314121811
dc.date.updated2012-10-28T11:18:52Z
item.grantfulltextopen-
item.fulltextWith Fulltext-
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