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Schema matching is a basic problem in many
database application domains, such as data integration, Ebusiness,
data warehousing, and semantic query processing.
In current implementations, schema matching is typically performed
manually, which has significant limitations. On the
other hand, previous research papers have proposed many
techniques to achieve a partial automation of the match operation
for specific application domains. We present a taxonomy
that covers many of these existing approaches, and we
describe the approaches in some detail. In particular,we distinguish
between schema-level and instance-level, element-level
and structure-level, and language-based and constraint-based
matchers. Based on our classification we review some previous
match implementations thereby indicating which part
of the solution space they cover.We intend our taxonomy and
review of past work to be useful when comparing different approaches
to schema matching, when developing a new match
algorithm, and when implementing a schema matching component.