MUSKCA: Ontology merging system based on consensus and trust evaluation

MUSKCA: Ontology merging system based on consensus and trust evaluation

Fabien Amarger Catherine Roussey Ollivier Haemmerlé Nathalie Hernandez Romain Guillaume  

IRIT, UMR 5505 Université de Toulouse, UT2J 5 allées Antonio Machado F-31058 Toulouse Cedex, France

UR TSCF, Irstea, 9 av. Blaise Pascal CS 20085, 63172 Aubière, France

Corresponding Author Email: 
prenom.nom@univ-tlse2.fr; prenom.nom@irstea.fr
Page: 
313-344
|
DOI: 
https://doi.org/10.3166/RIA.32.313-344
Received: 
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Accepted: 
|
Published: 
30 June 2018
| Citation
Abstract: 

Today many datasets related to the same domain of interest are available on the web of Linked Data. These datasets can have variable quality, which makes them difficult to reuse. In this article, we present a novel approach for identifying knowledge shared by different datasets taking into account their quality. This approach is based on metrics used to evaluate the trust score of common elements extracted from various datasets. In this article we propose several metrics, one of them is based on the integral of Choquet. These metrics have been evaluated on a real case study from the agriculture domain.  

Keywords: 

ontology development, trust, non-ontological sources, ontology design pattern, ontology merging

1. Introduction
2. État de l’art sur la fusion de bases de connaissances
3. Processus général
4. Processus de fusion de bases de connaissances
5. Calcul de la confiance d’un candidat
6. Évaluation
7. Conclusion et perspectives
Remerciements
  References

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