Year: 2011
Type: article
Abstract: — In the context detection field, an important challenge is automatically detecting the user’s task, for providing contextualized and personalized user support. Several approaches have been proposed to perform task classification, all advocating the window title as the best discriminative feature. In this paper we present a new ontology-based task detection approach, and evaluate it against previous work. We show that knowledge intensive tasks cannot be accurately classified using only the window title. We argue that our approach allows classifying such tasks better, by providing feature combinations that can adapt to the domain and the degree of freedom in task execution.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1507651201