Publikation: Automatic Tag Enrichment for Points-of-Interest in Open Street Map
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The user experience of geo-search engines and map services heavily depends on the quality of the underlying data. This is especially an issue for crowd-sourced data as e.g., collected and offered by the Open Street Map (OSM) project. In this paper we are focusing on points-of-interests (POIs), such as restaurants, shops, hotels and leisure facilities. Many of those are incompletely tagged in OSM (missing e.g., the amenity tag), which leads to such POIs not showing up in respective search queries or not being displayed correctly on the map. We develop methods that can automatically infer tags characterizing POIs solely based on the POI names. The idea being that many POI names already contain sufficient information for tagging. For example, ‘Pizzeria Bella Italia’ and ‘Chau’s Wok’ most certainly refer to restaurants, whereas ‘Cut & Color’ is likely a hairdresser. We employ machine learning techniques to extrapolate such additional tag information; our approach yields an accuracy of more than 85% for the considered tags. Moreover, for restaurants, we aimed for extrapolation of the respective cuisine tag (italian, sushi, etc.). For more than 19.000 out of 28.000 restaurants in Germany lacking the cuisine tag, our approach assigned a cuisine. In a random sample of those assignments 98% of these appeared to be true.
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FUNKE, Stefan, Sabine STORANDT, 2017. Automatic Tag Enrichment for Points-of-Interest in Open Street Map. 15th International Symposium, W2GIS 2017. Shanghai, China, 8. Mai 2017 - 9. Mai 2017. In: BROSSET, David, ed. and others. Web and Wireless Geographical Information Systems : 15th International Symposium, W2GIS 2017, Shanghai, China, May 8-9, 2017, proceedings. Cham: Springer, 2017, pp. 3-18. Lecture Notes in Computer Science. 10181. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-55997-1. Available under: doi: 10.1007/978-3-319-55998-8_1BibTex
@inproceedings{Funke2017-03-22Autom-43523,
year={2017},
doi={10.1007/978-3-319-55998-8_1},
title={Automatic Tag Enrichment for Points-of-Interest in Open Street Map},
number={10181},
isbn={978-3-319-55997-1},
issn={0302-9743},
publisher={Springer},
address={Cham},
series={Lecture Notes in Computer Science},
booktitle={Web and Wireless Geographical Information Systems : 15th International Symposium, W2GIS 2017, Shanghai, China, May 8-9, 2017, proceedings},
pages={3--18},
editor={Brosset, David},
author={Funke, Stefan and Storandt, Sabine}
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<dcterms:abstract xml:lang="eng">The user experience of geo-search engines and map services heavily depends on the quality of the underlying data. This is especially an issue for crowd-sourced data as e.g., collected and offered by the Open Street Map (OSM) project. In this paper we are focusing on points-of-interests (POIs), such as restaurants, shops, hotels and leisure facilities. Many of those are incompletely tagged in OSM (missing e.g., the amenity tag), which leads to such POIs not showing up in respective search queries or not being displayed correctly on the map. We develop methods that can automatically infer tags characterizing POIs solely based on the POI names. The idea being that many POI names already contain sufficient information for tagging. For example, ‘Pizzeria Bella Italia’ and ‘Chau’s Wok’ most certainly refer to restaurants, whereas ‘Cut & Color’ is likely a hairdresser. We employ machine learning techniques to extrapolate such additional tag information; our approach yields an accuracy of more than 85% for the considered tags. Moreover, for restaurants, we aimed for extrapolation of the respective cuisine tag (italian, sushi, etc.). For more than 19.000 out of 28.000 restaurants in Germany lacking the cuisine tag, our approach assigned a cuisine. In a random sample of those assignments 98% of these appeared to be true.</dcterms:abstract>
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