Linking Streets in OpenStreetMap to Persons in Wikidata

Motivation

Streets are often named after famous or distinguished individuals who may have a direct connection to the specific location. While Wikidata has information about persons, OpenStreetMap has information about streets.

➔ Can we connect OpenStreetMap and Wikidata by linking streets in OpenStreetMap to persons in Wikidata?

Example

The Wilhemstraße in Berlin could potentially be named after several different Wilhelms. Due to his popularity and as he was born in Berlin, we assume that the Wilhelmstraße was named after Friedrich Wilhelm I.

We can confirm our decision by checking Wilhelmstraße on Wikidata and its "named after" property or by checking Wilhelmstraße on OpenStreetMap and its "name:etymology:wikidata" key. However, these properties are rarely used and Wikidata does not cover all streets in OpenStreetMap.

Problem Statement

Approach

Our method StreetToPerson is based on the following pipeline:

Knowledge Graph Preprocessing

We use four datasets created from Wikidata:

• Person index: given a term as input, returns the Wikidata IDs of all people that match it.
• Person occupation index: all occupations of a person (e.g., monarch or writer).
• Person location index: relevant locations such as a person’s birthplace.
• Spatial dependencies: containment relations between locations (e.g., Berlin is located in Germany).

Friedrich Wilhelm I. in the person occupation index and the person location index.

Street Name Truncation

We remove affixes (i.e., prefixes and suffixes) from the street name which are not related to the names of person (e.g., "street", "road", and "avenue"). Our list of suffixes is available on GitHub..

Candidate Retrieval

We query the person index with the truncated street name to retrieve a set of candidates.

Feature Extraction

We extract 30 features for a street-to-person pair (s,p):

• Link count: The number of links pointing to p in Wikipedia.
• Name: Four binary features about which part of the person index was used (e.g., first name or last name).
• Occupations: 20 frequent occupations and whether they were found in the person occupation index .
• Spatial relations: Five features representing whether there is a spatial relation between p and s: "born", "died", "buried", "educated at", and "work location".

Example of the spatial dependencies between the street "Wilhelmstraße" and the city Berlin. Arrows denote "located in" relations (e.g., Berlin is located in Germany).

Street-to-person Classifier

Using the 30 features, we train a random forest model that classifies a street-to-person pair as positive or negative.

Evaluation

Using Wikidata's "named after" property, we extract 4,799 pairs of German streets and persons they were named after. We use these pairs together with negative examples as training and test datasets.

Baselines

• TagMe, a traditional entity linking approach on short text fragments [Ferragina, 2010].
• Popularity Ranking (PopRank): Simpler version of StreetToPerson where we take the person with the highest link count.
• Relevance Ranking (RelRank): The only existing approach for street-to-person linking [Almeida, 2016].

Evaluation of the Classification on Wikidata

StreetToPerson clearly outperforms the baselines and achieves a precision and recall of more than 0.9.

Application of StreetToPerson on OpenStreetMap

We apply StreetToPerson on German streets in OpenStreetMap. For 669,304 streets, we find at least one candidate person. For 183,022 of those streets, one person is classified positively.

Some streets in OpenStreetMap denote the person they were named after using the "name:etymology:wikidata" key. We use these streets for estimating precision and recall of StreetToPerson on OpenStreetMap.

StreetToPerson achieves a precision of more than 0.9 and a recall of more than 0.6 on OpenStreetMap.

Citation

References

• [Almeida, 2016] Paulo Dias Almeida, Jorge Rocha, Andrea Ballatore, and Alexander Zipf. 2016. Where the Streets Have Known Names. In International Conference on Computational Science and Its Applications (ICCSA ’16), Vol. 9789. 1–12. https://doi.org/10.1007/978-3-319-42089-9_1
• [Ferragina, 2010] Paolo Ferragina and Ugo Scaiella. 2010. TAGME: On-the-fly Annotation of Short Text Fragments (by Wikipedia Entities). In Conference on Information and Knowledge Management (CIKM). 1625–1628.

Notes

• Source: Image of Paul Wilhelm
• Source: Image of Friedrich Wilhelm I.
• Source: Image of Wilhelm Busch
• OpenStreetMap, the magnifying glass logo and State of the Map are registered trademarks of the OpenStreetMap Foundation

Acknowledgements

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).