Efficient query construction for large scale data

In recent years, a number of open databases have emerged on the Web, providing Web users with platforms to collaboratively create structured information. As these databases are intended to accommodate heterogeneous information and knowledge, they usually comprise a very large schema and billions of instances. Browsing and searching data on such a scale is not an easy task for a Web user. In this context, interactive query construction offers an intuitive interface for novice users to retrieve information from databases neither requiring any knowledge of structured query languages, nor any prior knowledge of the database schema. However, the existing mechanisms do not scale well on large scale datasets. This paper presents a set of techniques to boost the scalability of interactive query construction, from the perspective of both, user interaction cost and performance. We connect an abstract ontology layer to the database schema to shorten the process of user-computer interaction. We also introduce a search mechanism to enable efficient exploration of query interpretation spaces over large scale data. Extensive experiments show that our approach scales well on Freebase - an open database containing more than 7,000 relational tables in more than 100 domains.