Authors:
Claire Ponciano
1
;
Markus Schaffert
1
;
Falk Würriehausen
2
and
Jean-Jacques Ponciano
1
Affiliations:
1
i3mainz – Institute for Spatial Information and Surveying Technology, Mainz University of Applied Sciences, Lucy-Hillebrand-Straße 2, 55128 Mainz, Germany
;
2
Federal Agency for Cartography and Geodesy, Richard-Strauss-Allee 11, 60598 Frankfurt am Main, Germany
Keyword(s):
Semantic Interpretation, Linked Open Data, SPARQL, Ontology, Spatial Data, Semantic Web, Neural Machine Translation.
Abstract:
The rapid growth of geospatial data (at least 20% every year) makes spatial data increasingly heterogeneous. With the emergence of Semantic Web technologies, more and more approaches are trying to group these data in knowledge graphs, allowing to link data together and to facilitate their sharing, use and maintenance. These approaches face the problem of homogenisation of these data which are not unified in the structure of the data on the one hand and on the other hand have a vocabulary that varies greatly depending on the application domain for which the data are dedicated and the language in which they are described. In order to solve this problem of homogenisation, we present in this paper the foundations of a framework allowing to group efficiently heterogeneous spatial data in a knowledge base. This knowledge base is based on an ontology linked to Schema.org and DCAT-AP, and provides a data structure compatible with GeoSPARQL. This framework allows the integration of geospatial
data independently of their original language by translating them using Neural Machine Translation.
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