Institut für Raumbezogene Informations- und Messtechnik
Hochschule Mainz - University of Applied Sciences

Jean-Jacques Ponciano, M. Sc.

Jean-Jacques Ponciano, M. Sc.

Fachbereich Technik –
Geoinformatik & Vermessung
Raum: C 1.22
Telefon: +49 6131-628-1491
Fax: +49 6131-628-91491

Funktionen

  • – Wissenschaftliche Hilfskraft
Anzeigename: 
Jean-Jacques Ponciano, M. Sc.

E-Mail

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Telefon: 
+49 6131-628-1491
Fax: 
+49 6131-628-91491
Raum: 
C 1.22
Funktionen: 
Wissenschaftliche Hilfskraft

Projekte

The KnowDIP project aims at the conception of a framework for an automatic object detection in unstructured and heterogeneous data. This framework uses a representation of human…

Publikationen

Automatic Integration of Spatial Data into the Semantic Web

2017

Prudhomme, C.,
Homburg, T.,
Ponciano, J.J.,
Boochs, F.,
Roxin, A.,
Cruz, C.

BibTex

WebIST 2017

For several years, many researchers tried to semantically integrate geospatial datasets into the semantic web. Although, there are many general means of integrating interconnected relational datasets (e.g. R2RML), importing schema-less relational geospatial data remains a major challenge in the semantic web community. In our project SemGIS we face significant importation challenges of schema-less geodatasets, in various data formats without relations to the semantic web. We therefore developed an automatic process of semantification for aforementioned data using among others the geometry of spatial objects. We combine Natural Language processing with geographic and semantic tools in order to extract semantic information of spatial data into a local ontology linked to existing semantic web resources. For our experiments, we used LinkedGeoData and Geonames ontologies to link semantic spatial information and compared links with DBpedia and Wikidata for other types of information. The aim of our experiments presented in this paper, is to examine the feasibility and limits of an automated integration of spatial data into a semantic knowledge base and to assess its correctness according to different open datasets. Other ways to link these open datasets have been applied and we used the different results for evaluating our automatic approach.


Knowledge based Object Detection in Images and Point clouds

2016

Ponciano, J.J.,
Boochs, F.,
Trémeau, A.

BibTex

Molas 2016

Detection and classification of railway switches in point clouds of the German railway system

2016

Ponciano, J.J.,
Prudhomme, C.,
Boochs, F.

BibTex

n.A.