Publikation: Matching Points of Interest from Different Social Networking Sites
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2012
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GLIMM, Birte, ed., Antonio KRÜGER, ed.. KI 2012: Advances in Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 245-248. Lecture Notes in Computer Science. 7526. ISBN 978-3-642-33346-0. Available under: doi: 10.1007/978-3-642-33347-7_24
Zusammenfassung
Valuable user-generated information about locations (points of interest, POIs) is stored in various online social media platforms. Merging the data associated with one POI is hard because the platforms lack common identifiers. In addition, user-generated data is commonly faulty or contradictory. Here we present an approach matching POIs from Qype and Facebook Places to their counterparts in OpenStreetMap. The algorithm uses different similarity measures taking the geographic distance of POIs into account as well as the string similarity of selected metadata fields, showing good results.
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400 Sprachwissenschaft, Linguistik
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Data Integration, Social Networks, User-Generated Content, Points of Interest
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SCHEFFLER, Tatjana, Rafael SCHIRRU, Paul LEHMANN, 2012. Matching Points of Interest from Different Social Networking Sites. In: GLIMM, Birte, ed., Antonio KRÜGER, ed.. KI 2012: Advances in Artificial Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 245-248. Lecture Notes in Computer Science. 7526. ISBN 978-3-642-33346-0. Available under: doi: 10.1007/978-3-642-33347-7_24BibTex
@inproceedings{Scheffler2012Match-29020,
year={2012},
doi={10.1007/978-3-642-33347-7_24},
title={Matching Points of Interest from Different Social Networking Sites},
number={7526},
isbn={978-3-642-33346-0},
publisher={Springer Berlin Heidelberg},
address={Berlin, Heidelberg},
series={Lecture Notes in Computer Science},
booktitle={KI 2012: Advances in Artificial Intelligence},
pages={245--248},
editor={Glimm, Birte and Krüger, Antonio},
author={Scheffler, Tatjana and Schirru, Rafael and Lehmann, Paul}
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<dcterms:abstract xml:lang="eng">Valuable user-generated information about locations (points of interest, POIs) is stored in various online social media platforms. Merging the data associated with one POI is hard because the platforms lack common identifiers. In addition, user-generated data is commonly faulty or contradictory. Here we present an approach matching POIs from Qype and Facebook Places to their counterparts in OpenStreetMap. The algorithm uses different similarity measures taking the geographic distance of POIs into account as well as the string similarity of selected metadata fields, showing good results.</dcterms:abstract>
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