Coarse to fine keyword queries with user interactions

A large amount of linked data is now available, but to retrieve knowledge from these data, queries have to be formulated using formal query languages. While expressive query languages are developed, their use by end users, generally not familiar with formal languages, is limited. Keyword-based search is considered as a convenient and intuitive way for users to express their information needs. Keyword search over structured data is thus an interesting alternative but which raises challenging issues. The main challenge is to determine the meaning of a keyword query in order to translate it into a target formal query language, SPARQL in our case. In this paper, we address this challenge and propose a novel approach that relies on user interactions to determine the correct interpretation of the keyword query. The principle is to first ask the user to define a coarse keyword query, then to suggest candidate interpretations expressed in an explicit, thus unambiguous, and human readable form. Once the correct interpretation has been selected, the query may be refined with aggregate functions and comparatives. Experiments conducted on a large knowledge base show the effectiveness and the efficiency of the proposed approach.