The terms schema matching and mapping are often used interchangeably for a database process.
Among others, common challenges to automating matching and mapping have been previously classified in[1] especially for relational DB schemas; and in[2] – a fairly comprehensive list of heterogeneity not limited to the relational model recognizing schematic vs semantic differences/heterogeneity.
This process is made harder due to heterogeneities at the following levels[3] [4][5][6][7][8] Discusses a generic methodology for the task of schema integration or the activities involved.
Language-based or linguistic matchers use names and text (i.e., words or sentences) to find semantically similar schema elements.
Constraints like zipcodes must be 5 digits long or format of phone numbers may allow matching of such types of instance data.
The motivation for this work is that structures or substructures often repeat, for example in schemas in the E-commerce domain.
[4][5] The relationship types between objects that are identified at the end of a matching process are typically those with set semantics such as overlap, disjointness, exclusion, equivalence, or subsumption.
Among others, an early attempt to use description logics for schema integration and identifying such relationships was presented.