=Paper=
{{Paper
|id=Vol-1111/om2013_poster10
|storemode=property
|title=Interlinking and visualizing linked open data with geospatial reference data
|pdfUrl=https://ceur-ws.org/Vol-1111/om2013_poster10.pdf
|volume=Vol-1111
|dblpUrl=https://dblp.org/rec/conf/semweb/FeliachiAHA13
}}
==Interlinking and visualizing linked open data with geospatial reference data==
https://ceur-ws.org/Vol-1111/om2013_poster10.pdf
Interlinking and Visualizing Linked Open Data
with Geospatial Reference Data
Abdelfettah Feliachi1 , Nathalie Abadie1 , Fayçal Hamdi2 , and Ghislain Auguste
Atemezing3
1
IGN, COGIT, 73 Avenue de Paris, 94165 Saint-Mandé, France
2
CEDRIC, CNAM, F-75141 Paris Cedex 03, France
3
EURECOM, Multimedia Department, Campus SophiaTech, France
1 Context and purposes
An increasing number of thematic datasets are published as RDF graphs and
linked to other datasets by identifying equivalent resources in other relevant
datasets. Among the set of properties usually used as data linking criteria, ge-
olocation (addresses, locations, coordinates ) remains one of the most commonly
used.
However, resources that actually refer to complex topographic features are
generally described by very simple geolocation properties, such as a position de-
fined by coordinates (long, lat). On the other hand, geographic reference datasets
provide more precise geometric information about geographic features. Inter-
linking thematic linked open datasets with geographic reference datasets would
enable us to take advantage of both information sources to link independent the-
matic datasets and create rich cartographic applications for data visualization.
This data linking task is generally performed by comparing properties values
of each resource of a given data set, with homologous properties of the resources
described in other datasets [3]. In the field of geographic databases, data match-
ing is also performed by comparing properties, and especially complex geometries
(curves, lines, polygons) that are used to represent the shape and the location
of geographic features. This task is usually based on distance measures chosen
according to the type of the geometric primitives that must be compared [1,
2, 4, 5]. We aim at combining both approaches to link both thematic and geo-
graphical reference data and exploit the generated links in a data visualization
application.
2 Approach and use case
In order to take advantage of existing data linking tools, we have converted
geographic shape data and stored them into a RDF triple store. This task has
been achieved by using the Datalift4 platform that also enables to perform the
linking process with external published datasets, through the use of Silk5 linking
tool. Our linking approach is mainly based on geolocation properties comparison.
4
http://datalift.org/
5
https://www.assembla.com/spaces/silk/wiki/Silk_Workbench
2 Interlinking and Visualizing Linked Open Data
Thus we have added to Silk more GIS distance measures for computing the
shortest distance between any geometric primitive and simple position locations
used in thematic datasets.
The result of this interlinking process is a list of owl:sameAs links between
entities of each datasets, at a given threshold. These links are used to extend
the geographic reference data set with information queried on the fly from the
external thematic datasets through the visualization interface. We have applied
this approach on a geographical reference dataset about buildings and data about
historical monuments extracted from French DBpedia6 , on the area of Paris.
Fig. 1. DBpedia points locating historical monuments linked with polygons describing
buildings in a geographic reference dataset.
3 Conclusion
The use of links between thematic and reference data could be further investi-
gated to enable data visualization at different level of detail, and visual detection
errors during matching process of geodata.
References
1. Mustire, S. et Devogele, T. Matching networks with different levels of detail.
GeoInfor-matica, paratre en 2008
2. Olteanu, A.-M. Appariement de donnes spatiales par prise en compte de connais-
sances imprcises. Thse de doctorat. Universit de Marne-La-Valle, 2008
3. Scharffe, F., Euzenat, J.: Mthodes et outils pour lier le Web des donnes. RFIA 2010:
Re-connaissance des Formes et Intelligence Artificielle (2010)
4. Voltz, S. An Iterative Approach for Matching Multiple Representations of Street
Data. In : Proceedings of ISPRS Workshop, Multiple representation and interoper-
ability of spatial data, Hanovre (Allemagne), 22-24 fvrier 2006, p. 101-110
5. Walter, V. et Fritsch, D. Matching Spatial datasets: Statistical Approach. Interna-
tional Journal of Geographical Information Science, 1999, 13(5), p. 445-473
6
http://fr.dbpedia.org