KD2R: a key discovery method for semantic reference reconciliation

Authors:
Danai Symeonidou;Nathalie Pernelle;Fatiha Saïs
Affiliations:
LRI, CNRS & Paris-Sud XI University, INRIA Saclay, Orsay, Cedex, France;LRI, CNRS & Paris-Sud XI University, INRIA Saclay, Orsay, Cedex, France;LRI, CNRS & Paris-Sud XI University, INRIA Saclay, Orsay, Cedex, France
Venue:
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems
Year:
2011

Citing 6
Cited 4

A knowledge-based approach for duplicate elimination in data cleaning

Information Systems - Data extraction, cleaning and reconciliation
Reference reconciliation in complex information spaces

Proceedings of the 2005 ACM SIGMOD international conference on Management of data
GORDIAN: efficient and scalable discovery of composite keys

VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Duplicate Record Detection: A Survey

IEEE Transactions on Knowledge and Data Engineering
Collective entity resolution in relational data

ACM Transactions on Knowledge Discovery from Data (TKDD)
Combining a Logical and a Numerical Method for Data Reconciliation

Journal on Data Semantics XII

Keys and pseudo-keys detection for web datasets cleansing and interlinking

EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
N2R-part: identity link discovery using partially aligned ontologies

Proceedings of the 2nd International Workshop on Open Data
Discovering keys in RDF/OWL dataset with KD2R

Proceedings of the 2nd International Workshop on Open Data
An automatic key discovery approach for data linking

Web Semantics: Science, Services and Agents on the World Wide Web

Quantified Score

Hi-index	0.00

Visualization

Abstract

The reference reconciliation problem consists of deciding whether different identifiers refer to the same world entity. Some existing reference reconciliation approaches use key constraints to infer reconciliation decisions. In the context of the Linked Open Data, this knowledge is not available. We propose KD2R, a method which allows automatic discovery of key constraints associated to OWL2 classes. These keys are discovered from RDF data which can be incomplete. The proposed algorithm allows this discovery without having to scan all the data. KD2R has been tested on data sets of the international contest OAEI and obtains promising results.