Inferring Same-as Facts from Linked Data: An Iterative Import-by-Query Approach

Mustafa Al-Bakri, Manuel Atencia, Steffen Lalande, Marie-Christine Rousset

AAAI 2015 pp. 9-15

doi:10.1609/AAAI.V29I1.9174 /aaai/2015/albakri2015aaai-inferring/

Abstract

In this paper we model the problem of data linkage in Linked Data as a reasoning problem on possibly decentralized data. We describe a novel import-by-query algorithm that alternates steps of sub-query rewriting and of tailored querying the Linked Data cloud in order to import data as specific as possible for inferring or contradicting given target same-as facts. Experiments conducted on a real-world dataset have demonstrated the feasibility of this approach and its usefulness in practice for data linkage and disambiguation.

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Cite

Text

Al-Bakri et al. "Inferring Same-as Facts from Linked Data: An Iterative Import-by-Query Approach." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9174

Markdown

[Al-Bakri et al. "Inferring Same-as Facts from Linked Data: An Iterative Import-by-Query Approach." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/albakri2015aaai-inferring/) doi:10.1609/AAAI.V29I1.9174

BibTeX

@inproceedings{albakri2015aaai-inferring,
  title     = {{Inferring Same-as Facts from Linked Data: An Iterative Import-by-Query Approach}},
  author    = {Al-Bakri, Mustafa and Atencia, Manuel and Lalande, Steffen and Rousset, Marie-Christine},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {9-15},
  doi       = {10.1609/AAAI.V29I1.9174},
  url       = {https://mlanthology.org/aaai/2015/albakri2015aaai-inferring/}
}