Thesis Open Access

Semi-Automatic schema matching: challenges and a composable match based solution

Bottelier, Jordy

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<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Bottelier, Jordy</dc:creator>
  <dc:description>During data integration it often occurs that two databases with different schemas have to be integrated. This process is called schema matching. Automating part of or the entire processes of schema matching can essentially accelerate the data integration procedure of human experts and thus reduce the overall time cost. A semi-automated solution could be that a system predicts the mapping based on the schema contents, a human expert could then evaluate the predicted mapping.

This thesis discusses a highly configurable framework that utilizes hierarchical classification in order to match schemas. The experiments performed within this thesis show that the configurability and hierarchical classification improves the matching result, and it proposes an algorithm to automatically optimize such a hierarchy (pipeline).</dc:description>
  <dc:subject>Schema matching; hierarchical classification; machine learning; software engineering; framework</dc:subject>
  <dc:title>Semi-Automatic schema matching: challenges and a composable match based solution</dc:title>
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