Details
| Original language | English |
|---|---|
| Title of host publication | WWW´22 |
| Subtitle of host publication | Companion Proceedings of the Web Conference 2022 |
| Pages | 294-297 |
| Number of pages | 4 |
| ISBN (electronic) | 9781450391306 |
| Publication status | Published - 16 Aug 2022 |
| Event | 31st ACM Web Conference, WWW 2022 - Virtual, Online, France Duration: 25 Apr 2022 → 29 Apr 2022 |
Abstract
Geographic web sources such as OpenStreetMap (OSM) and knowledge graphs such as Wikidata are often unconnected. An example connection that can be established between these sources are links between streets in OSM to the persons in Wikidata they were named after. This paper presents StreetToPerson, an approach for connecting streets in OSM to persons in a knowledge graph based on relations in the knowledge graph and spatial dependencies. Our evaluation shows that we outperform existing approaches by 26 percentage points. In addition, we apply StreetToPerson on all OSM streets in Germany, for which we identify more than 180,000 links between streets and persons.
Keywords
- Knowledge graphs, OpenStreetMap, Street names, Wikidata
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Software
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WWW´22: Companion Proceedings of the Web Conference 2022. 2022. p. 294-297.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Linking Streets in OpenStreetMap to Persons in Wikidata
AU - Gurtovoy, Daria
AU - Gottschalk, Simon
N1 - Funding Information: This work was partially funded by the Federal Ministry of Education and Research (BMBF), Germany under “Simple-ML” (01IS18054) and the DFG, German Research Foundation, under “WorldKG” (424985896).
PY - 2022/8/16
Y1 - 2022/8/16
N2 - Geographic web sources such as OpenStreetMap (OSM) and knowledge graphs such as Wikidata are often unconnected. An example connection that can be established between these sources are links between streets in OSM to the persons in Wikidata they were named after. This paper presents StreetToPerson, an approach for connecting streets in OSM to persons in a knowledge graph based on relations in the knowledge graph and spatial dependencies. Our evaluation shows that we outperform existing approaches by 26 percentage points. In addition, we apply StreetToPerson on all OSM streets in Germany, for which we identify more than 180,000 links between streets and persons.
AB - Geographic web sources such as OpenStreetMap (OSM) and knowledge graphs such as Wikidata are often unconnected. An example connection that can be established between these sources are links between streets in OSM to the persons in Wikidata they were named after. This paper presents StreetToPerson, an approach for connecting streets in OSM to persons in a knowledge graph based on relations in the knowledge graph and spatial dependencies. Our evaluation shows that we outperform existing approaches by 26 percentage points. In addition, we apply StreetToPerson on all OSM streets in Germany, for which we identify more than 180,000 links between streets and persons.
KW - Knowledge graphs
KW - OpenStreetMap
KW - Street names
KW - Wikidata
UR - http://www.scopus.com/inward/record.url?scp=85137417589&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2302.12907
DO - 10.48550/arXiv.2302.12907
M3 - Conference contribution
AN - SCOPUS:85137417589
SP - 294
EP - 297
BT - WWW´22
T2 - 31st ACM Web Conference, WWW 2022
Y2 - 25 April 2022 through 29 April 2022
ER -