ML Anthology

L2R: A Logical Method for Reference Reconciliation

Fatiha Saïs, Nathalie Pernelle, Marie-Christine Rousset
AAAI 2007 pp. 329-334
/aaai/2007/sais2007aaai-l/

Abstract

The reference reconciliation problem consists in deciding whether different identifiers refer to the same data, i.e., correspond to the same world entity. The L2R system exploits the semantics of a rich data model, which extends RDFS by a fragment of OWL-DL and SWRL rules. In L2R, the semantics of the schema is translated into a set of logical rules of reconciliation, which are then used to infer correct decisions both of reconciliation and no reconciliation. In contrast with other approaches, the L2R method has a precision of 100 % by construction. First experiments show promising results for recall, and most importantly significant increases when rules are added.

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Text

Saïs et al. "L2R: A Logical Method for Reference Reconciliation." AAAI Conference on Artificial Intelligence, 2007.

Markdown

[Saïs et al. "L2R: A Logical Method for Reference Reconciliation." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/sais2007aaai-l/)

BibTeX

@inproceedings{sais2007aaai-l,
  title     = {{L2R: A Logical Method for Reference Reconciliation}},
  author    = {Saïs, Fatiha and Pernelle, Nathalie and Rousset, Marie-Christine},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2007},
  pages     = {329-334},
  url       = {https://mlanthology.org/aaai/2007/sais2007aaai-l/}
}