  Schema Matching is a method of finding attributes that are either similar to
each other linguistically or represent the same information. In this project,
we take a hybrid approach at solving this problem by making use of both the
provided data and the schema name to perform one to one schema matching and
introduce the creation of a global dictionary to achieve one to many schema
matching. We experiment with two methods of one to one matching and compare
both based on their F-scores, precision, and recall. We also compare our method
with the ones previously suggested and highlight differences between them.
