DMCA
Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach (2001)
| Venue: | In SIGMOD Conference |
| Citations: | 424 - 50 self |
BibTeX
@INPROCEEDINGS{Doan01reconcilingschemas,
author = {Anhai Doan and Pedro Domingos and Alon Halevy},
title = {Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach},
booktitle = {In SIGMOD Conference},
year = {2001},
pages = {509--520}
}
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Abstract
A data-integration system provides access to a multitude of data sources through a single mediated schema. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the source schemas and the mediated schema. We describe LSD, a system that employs and extends current machine-learning techniques to semi-automatically find such mappings. LSD first asks the user to provide the semantic mappings for a small set of data sources, then uses these mappings together with the sources to train a set of learners. Each learner exploits a different type of information either in the source schemas or in their data. Once the learners have been trained, LSD nds semantic mappings for a new data source by applying the learners, then combining their predictions using a meta-learner. To further improve matching accuracy, we extend machine learning techniques so that LSD can incorporate domain constraints as an additional source of knowledge, and develop a novel learner that utilizes the structural information in XML documents. Our approach thus is distinguished in that it incorporates multiple types of knowledge. Importantly, its architecture is extensible to additional learners that may exploit new kinds of information. We describe a set of experiments on several real-world domains, and show that LSD proposes semantic mappings with a high degree of accuracy.
Keyphrases
semantic mapping disparate data source machine-learning approach source schema data source domain constraint building system several real-world domain current machine-learning technique key bottleneck laborious manual construction high degree new kind data-integration system small set machine learning technique mediated schema additional source novel learner structural information new data source multiple type additional learner different type xml document
