A tremendous amount of data is being generated every day both on the Web and in public and private organisations; and, by all accounts, in this increasingly data-oriented world, any individual or organisation, who posses the necessary knowledge, skills, and tools to make value out of data at such scales, bears a considerable advantage in terms of competitiveness and development. Particularly, in an enterprise setting, ability to access and use data in business processes such as sense-making and intelligence analysis is key for its value creation potential.
Today, however, data access still stands as a major bottleneck for many organisations. This is mostly due to the sharp distinction between employees who have technical skills and knowledge to extract data (i.e., database/IT experts, skilled users etc.) and those who have domain knowledge and know how to interpret and use data (i.e., domain experts, end-users etc.). The result is a workflow where domain-experts either have to use pre-defined queries embedded in applications or communicate their information needs to database-experts. In such a workflow, the turn-around time from users’ initial information needs to receiving the answer can be in the range of weeks, incurring significant costs.
Approaches that eliminate the man-in-the-middle and allow end-users to directly engage with data and extract it on their own, have been of interest to researchers for many years. As anticipated, for end-users, the accessibility of traditional structured query languages such as SQL and XQuery fall far short, since such textual languages do require end-users to have a set of technical skills and to recall domain concepts and the terminology and syntax of the language being used. For this very reason, visual query systems and languages have emerged to alleviate the end-user data access problem. A visual system or language follows the direct manipulation idea, where the domain and query language are represented with a set of visual elements.
In this respect, we have been developing a visual query formulation tool, OptiqueVQS (see the video above), for naive end-users. However, even with considerably small ontologies, the number of ontology elements to choose from increases drastically, and hence hinders usability. Therefore, in this thesis, you are expected to implement a method using the log of past queries for ranking and suggesting query extensions as a user formulates a query.
