In this paper we present a proposal for the implementation and evaluation of a novel method for automatically using data-rich text for filling form-based input interfaces. Our solution takes a text as input, extracts implicit data values from it and fills appropriate fields. For this task, we rely on knowledge obtained from values of previous submissions for each field, which are freely obtained from the usage of the interfaces. Our approach, called iForm , exploits features related to the content and the style of these values, which are combined through a Bayesian framework. Through extensive experimentation, we show that our approach is feasible and effective, and that it works well even when only a few previous submissions to the input interface are available.

On the web of today the most prevalent solution for users to interact with data-intensive applications is the use of form-based interfaces composed by several data input fields, such as text boxes, radio buttons, pull-down lists, check boxes, etc. Although these interfaces are popular and effective, in many cases, free text interfaces are preferred over form-based ones. In this paper we discuss the proposal and the implementation of a novel IR-based method for using data rich free text to interact with form-based interfaces. Our solution takes a free text as input, extracts implicitly data values from it and fills appropriate fields using them. For this task, we rely on values of previous submissions for each field, which are freely obtained from the usage of form-based interfaces