A Machine Learning Approach to Foreign Key Discovery
Source: Humboldt-Universitat zu Berlin
The authors study the problem of automatically discovering semantic associations between schema elements, namely foreign keys. This problem is important in all applications where data sets need to be integrated that are structured in tables but without explicit foreign key constraints. If such constraints could be recovered automatically, querying and integrating such databases would become much easier. Clearly, one may find candidates for foreign key constraints in a given database instance by computing all INclusion Dependencies (IND) between attributes.