Modern society depends on the access to a wide range of information that is located
in heterogeneous data sources. Schema matching is a task of finding relationships
among data source elements automatically. However, most of the existing schema
matching software are semi-automatic meaning that they need a lot of interaction
from an expert familiar with the systems being integrated. In this work, we propose
a new hybrid matcher algorithm, called TRC-matcher, that is targeted for matching
business oriented XML schemas with none or minor user assistance. When compared
to previously published schema matching methods, the efficiency of the new
algorithm is based on a new content profiling algorithm and on intelligent combination
of matching results of multiple matching algorithms. In addition, an enhanced
version of the TRC-Matcher is introduced that combines machine learning methods
together with few new matching algorithms.