The Design Space of a Configurable Autocompletion Component

Autocompletion is a commonly used interface feature in di- verse applications. Semantic Web data has, on the one hand, the potential to provide new functionality by exploiting the semantics in the data used for generating autocompletion suggestions. Semantic Web applications, on the other hand, typically pose extra requirements on the semantic proper- ties of the suggestions given. When the number of syntactic matches becomes too large, some means of selecting a se- mantically meaningful subset of suggestions to be presented to the user is needed. In this paper we identify a number of key design dimensions of autocompletion interface com- ponents. Our hypothesis is that a one-size-fits-all solution to autocompletion interface components does not exist, be- cause dierent tasks and dierent data sets require inter- faces corresponding to dierent points in our design space. We present a fully configurable architecture, which can be used to configure autocompletion components to the desired point in this design space. The architecture has been im- plemented as an open source software component that can be plugged into a variety of applications. We report on the results of a user evaluation that confirms this hypothesis, and describe the need to evaluate semantic autocompletion in a task and application-specific context.