Structure-aware indexing for keyword search in databases.

Most of existing methods of keyword search over relational databases find the Steiner trees composed of relevant tuples as the answers. They identify the Steiner trees by discovering the rich structural relationships between tuples, and neglect the fact that such structural relationships can be pre-computed and indexed. Tuple units that are composed of most relevant tuples are proposed to address this problem. Tuple units can be precomputed and indexed. Existing methods identify a single tuple unit to answer keyword queries. They, however, may involve false negatives as in many cases a single tuple unit cannot answer a keyword query. Instead, multiple tuple units should be integrated to answer keyword queries. To address this problem, in this paper, we study how to integrate multiple related tuple units to effectively answer keyword queries. We devise novel indices and incorporate the structural relationships between different tuple units into the indices. We use the indices to efficiently and progressively identify the top-k relevant answers. We have implemented our method in real database systems, and the experimental results show that our approach achieves high search efficiency and accuracy, and outperforms state-of-the-art methods significantly.