Anelli, V.W. and Calì, Andrea and Di Noia, T. and Palmonari, M. and Ragone, A. (2016) Exposing open street map in the linked data cloud. In: Fujita, H. and Ali, M. and Selamat, A. and Sasaki, J. and Kurematsu, M. (eds.) Trends in Applied Knowledge-Based Systems and Data Science. Lecture Notes in Computer Science 9799. Cham, Switzerland: Springer, pp. 344-355. ISBN 9783319420066.
Abstract
After the mobile revolution, geographical knowledge has getting more and more importance in many location-aware application scenarios. Its popularity influenced also the production and publication of dedicated datasets in the Linked Data (LD) cloud. In fact, its most recent representation shows Geonames competing with DBpedia as the largest and most linked knowledge graph available in the Web. Among the various projects related to the collection and publication of geographical information, as of today, Open Street Map (OSM) is for sure one of the most complete and mature one exposing a huge amount of data which is continually updated in a crowdsourced fashion. In order to make all this knowledge available as Linked Data, we developed LOSM: a SPARQL endpoint able to query the data available in OSM by an on-line translation form syntax to a sequence of calls to the OSM overpass API. The endpoint makes also possible an on-the-fly integration among Open Street Map information and the one contained in external knowledge graphs such as DBpedia, Freebase or Wikidata.
Metadata
| Item Type: | Book Section |
|---|---|
| Additional Information: | 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Morioka, Japan, August 2-4, 2016, Proceedings |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Data Analytics, Birkbeck Institute for |
| Depositing User: | Administrator |
| Date Deposited: | 14 Dec 2016 12:36 |
| Last Modified: | 09 Aug 2023 12:41 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/17636 |
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