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

Claire Prudhomme, M. Sc.

Claire Prudhomme, M. Sc.

Fachbereich Technik –
Geoinformatik & Vermessung
Raum: C1.22
Telefon: +49 6131-628-1454
Fax: +49 6131-628-91454

Funktionen

  • – Wissenschaftliche Mitarbeiterin
Anzeigename: 
Claire Prudhomme, M. Sc.

E-Mail

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Telefon: 
+49 6131-628-1454
Fax: 
+49 6131-628-91454
Raum: 
C1.22
Funktionen: 
Wissenschaftliche Mitarbeiterin

Projekte

The SemGIS project aims at interpreting heterogeneous data by creating interoperability via a semantic layer between former unrelated spatial and non-spatial data sets.…
The presented project is an internship project by two french master’s degree students. It has been realized in partnership with the German railway company, Deutsche Bahn. The…

Publikationen

Towards the design of respond action in disaster management using knowledge modeling

2017

Prudhomme, C.,
Roxin, A.,
Cruz, C.,
Boochs, F.

BibTex

The Fourth International Conference on Information Systems for Crisis Response and Management in Mediterranean Countries (ISCRAM-med 2017)

This position paper highlights current problems linked to the aspects of the multi-agency collaboration during disaster response. The coordination and cooperation depend on the information sharing and use which must face up to interoperability, access rights, and quality problems. The research project aims at providing an assessment of information impact on the disaster response in order to support the decisionmaking about what information shared or what quality of data used to improve the response efficiency. Our research approach propose to combine an information system able to integrate heterogeneous data and a simulation system to assess different strategies of information sharing, dissemination and use. A knowledge base is used as a bridge between information system and simulation system. This knowledge base allows for designing dynamically a simulation according to open data and for managing the own knowledge and information known by each agent.


Integration, quality assurance and usage of geospatial data with semantic tools

2017

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

BibTex

gis.Science

Katastrophenmanagement: Die geflutete Stadt

2017

Prudhomme, C.

BibTex

n.A.

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.


Vorstellung SemGIS Projekt - Einblick und Status

2016

Homburg, T.,
Prudhomme, C.

BibTex

n.A.

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.

SEMANTIC CATALOGUE TO MANAGE DATA SOURCES IN DISASTER MANAGEMENT SYSTEM

2016

Prudhomme, C.,
Roxin, A.,
Cruz, C.,
Boochs, F.

BibTex

The 15th International Conference on Informatics in Economy 2016

With the climate change, disasters occur more frequently and the need for efficient disaster management systems becomes highly recommended to save lives. This paper deals with a study of existing systems, with the intention of determining the main recent improvement in the domain. The heterogeneous data integration process is a major central point. Thus, a semantic system with three main components is proposed as a new position in the disaster management systems. These three components are a knowledge base, a reasoner and a semantic catalogue. The knowledge base provides a controlled vocabulary and allows storing information retrieval. The semantic catalogue facilitates the access to data sources adapted to user's and agent's needs. The reasoner analyzes the information in the knowledge base thus replying to the user queries. In addition, the reasoner aims at adding automatically new data sources in the semantic catalogue. The fast access to a great number of data sources is of benefit for decision-making systems such as disaster management systems.


Interpreting Heterogenous Geospatial Data using Semantic Web Technologies

2016

Homburg, T.,
Prudhomme, C.,
Würriehausen, F.,
Karmacharya, A.,
Boochs, F.,
Cruz, C.,
Roxin, A.M.

BibTex

Computational Science and Its Applications -- ICCSA 2016

The paper presents work on implementation of semantic technologies within a geospatial environment to provide a common base for further semantic interpretation. The work adds on the current works in similar areas where priorities are more on spatial data integration. We assert that having a common unified semantic view on heterogeneous datasets provides a dimension that allows us to extend beyond conventional concepts of searchability, reusability, composability and interoperability of digital geospatial data. It provides contextual understanding on geodata that will enhance effective interpretations through possible reasoning capabilities.  We highlight this through use cases in disaster management and planned land use that are significantly different. This paper illustrates the work that firstly follows existing Semantic Web standards when dealing with vector geodata and secondly extends current standards when dealing with raster geodata and more advanced geospatial operations.