Schema Matching using Machine Learning
This work addresses schema matching for data integration tasks, but it appears incremental as it builds on existing methods with hybrid techniques.
The paper tackles the problem of schema matching by proposing a hybrid approach that uses both data and schema names for one-to-one matching and introduces a global dictionary for one-to-many matching, achieving results evaluated through F-scores, precision, and recall.
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.