A Hidden Markov Model Approach to Keyword-Based Search over Relational Databases

We present a novel method for translating keyword queries over relational databases into SQL queries with the same intended semantic meaning. In contrast to the majority of the existing keyword-based techniques, our approach does not require any a-priori knowledge of the data instance. It follows a probabilistic approach based on a Hidden Markov Model for computing the top-K best mappings of the query keywords into the database terms, i.e., tables, attributes and values. The mappings are then used to generate the SQL queries that are executed to produce the answer to the keyword query. The method has been implemented into a system called KEYRY (from KEYword to queRY).