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Conference papers

LDM: Link Discovery Method for new Resource Integration

Nathalie Pernelle 1, 2 Fatiha Saïs 1, 2 
2 LEO - Distributed and heterogeneous data and knowledge
UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : In this paper we address the problem of resource discovery in the Linked Open Data cloud (LOD) where data described by different schemas is not always linked. We propose an approach that allows discovery of new links between data. These links can help to match schemas that are conceptually relevant with respect to a given application domain. Furthermore, these links can be exploited during the querying process in order to combine data coming from different sources. In this approach we exploit the semantic knowledge declared in different schemas in order to model: (i) the influences between concept similarities, (ii) the influences between data similarities, and (iii) the influences between data and concept similarities. The similarity scores are computed by an iterative resolution of two non linear equation systems that express the concept similarity computation and the data similarity computation. The proposed approach is illustrated on scientific publication data.
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Submitted on : Friday, September 23, 2011 - 5:37:14 PM
Last modification on : Sunday, June 26, 2022 - 11:54:26 AM
Long-term archiving on: : Saturday, December 24, 2011 - 2:20:55 AM


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  • HAL Id : inria-00625689, version 1



Nathalie Pernelle, Fatiha Saïs. LDM: Link Discovery Method for new Resource Integration. Fourth International Workshop on Resource Discovery, May 2011, heraklion, Greece. pp 94-108. ⟨inria-00625689⟩



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