Database Search vs. Information Retrieval: A Novel Method for Studying Natural Language Querying of Semi-Structured Data

The traditional approach of querying a relational database is via a formal language, namely SQL. Recent developments in the design of natural language interfaces to databases show promising results for querying either with keywords or with full natural language queries and thus render relational databases more accessible to non-tech savvy users. Such enhanced relational databases basically use a search paradigm which is commonly used in the field of information retrieval. However, the way systems are evaluated in the database and the information retrieval communities often differs due to a lack of common benchmarks. In this paper, we provide an adapted benchmark data set that is based on a test collection originally used to evaluate information retrieval systems. The data set contains 45 information needs developed on the Internet Movie Database (IMDb), including corresponding relevance assessments. By mapping this benchmark data set to a relational database schema, we enable a novel way of directly comparing database search techniques with information retrieval. To demonstrate the feasibility of our approach, we present an experimental evaluation that compares SODA, a keyword-enabled relational database system, against the Terrier information retrieval system and thus lays the foundation for a future discussion of evaluating database systems that support natural language interfaces.