Less Data Delivers Higher Search Effectiveness for Keyword Queries

As many users, such as scientists, do not know the schema and/or content of their databases, they cannot precisely formulate their information needs using formal query languages, such as SQL. To help these users, researchers have proposed keyword query interfaces over which users can submit their information need using a set of keywords without the precise knowledge about the schema or content of the database. Despite their usability, keyword query interfaces suffer from low effectiveness in answering queries. Therefore, they may return many non-relevant answers or do not return many answers related to the input queries. It is well established that the effectiveness of answering queries decreases as the size of the dataset grows, given all other conditions are the same. In this paper, we propose an approach that uses only a relatively small subset of the database to answer most queries effectively. Since this subset may not contain the relevant answers to many queries, we also propose a method that predicts whether a query can be answered more effectively using this subset or the entire database. Our comprehensive empirical studies using multiple real-world databases and query workloads indicate that our approach significantly improves both the effectiveness and efficiency of answering queries.