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Improving Schema Matching with Linked Data

Ahmad Assaf 1, 2, * Eldad Louw 3 Aline Senart 2 Corentin Follenfant 2, 4 Raphaël Troncy 1 David Trastour 2 
* Corresponding author
4 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : With today's public data sets containing billions of data items, more and more companies are looking to integrate external data with their traditional enterprise data to improve business intelligence analysis. These distributed data sources however exhibit heterogeneous data formats and terminologies and may contain noisy data. In this paper, we present a novel framework that enables business users to semi-automatically perform data integration on potentially noisy tabular data. This framework offers an extension to Google Refine with novel schema matching algorithms leveraging Freebase rich types. First experiments show that using Linked Data to map cell values with instances and column headers with types improves significantly the quality of the matching results and therefore should lead to more informed decisions.
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Preprints, Working Papers, ...
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Contributor : Corentin Follenfant Connect in order to contact the contributor
Submitted on : Thursday, November 29, 2012 - 10:36:38 AM
Last modification on : Thursday, August 4, 2022 - 4:54:55 PM

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  • HAL Id : hal-00758681, version 1
  • ARXIV : 1205.2691



Ahmad Assaf, Eldad Louw, Aline Senart, Corentin Follenfant, Raphaël Troncy, et al.. Improving Schema Matching with Linked Data. 2012. ⟨hal-00758681⟩



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