ML Anthology

Learning to Handle Inconsistency for Multi-Source Integration

Sheila Tejada, Craig A. Knoblock, Steven Minton
AAAI 1999 pp. 983
/aaai/1999/tejada1999aaai-learning/

Abstract

Many problems arise when trying to integrate information from multiple sources on the web. One of these problems is that data instances can exist in inconsistent formats across several sources. An example application of information integration is trying to integrate all the reviews of Los Angeles restaurants from Yahoo's Restaurants webpage with the current health rating for each restaurant from the LA County Department of Health's website. Integrating these sources requires determining if they share any of the same restaurants by comparing the data instances from both sources (Figure 1). Because the instances can be in different formats, e.g. the restaurant Jerry's Famous Deli from Yahoo's webpage can appear as Jerry's Famous Delicatessen in the Dept. of Health's source, they can not be compared using equality; but must be judged according to similarity.

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Cite

Text

Tejada et al. "Learning to Handle Inconsistency for Multi-Source Integration." AAAI Conference on Artificial Intelligence, 1999.

Markdown

[Tejada et al. "Learning to Handle Inconsistency for Multi-Source Integration." AAAI Conference on Artificial Intelligence, 1999.](https://mlanthology.org/aaai/1999/tejada1999aaai-learning/)

BibTeX

@inproceedings{tejada1999aaai-learning,
  title     = {{Learning to Handle Inconsistency for Multi-Source Integration}},
  author    = {Tejada, Sheila and Knoblock, Craig A. and Minton, Steven},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {1999},
  pages     = {983},
  url       = {https://mlanthology.org/aaai/1999/tejada1999aaai-learning/}
}