Schema Evolution publication categorizer

Semantic validation of the effectiveness of a schema
matching system is traditionally performed by comparing
system-generated mappings with those of human evaluators.
The human effort required for validation quickly becomes
huge in large scale environments. The performance
of a matching system, however, is not solely determined by
the quality of the mappings, but also by the efficiency with
which it can produce them. Improving efficiency quickly
leads to a trade-off between efficiency and effectiveness.
Establishing or obtaining a large test collection for measuring

Schema matching attempts to discover semantic mappings between elements of two schemas. Elements are cross compared using various heuristics (e.g., name, data-type, and structure similarity). Seen from a broader perspective, the schema matching problem is a combinatorial problem with an exponential complexity. This makes the naive matching algorithms for large schemas prohibitively inefficient. In this paper we propose a clustering based technique for improving the efficiency of large scale schema matching.