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‘Understanding context is vital ’ [1] and ‘context is key ’ [2] signal the key interest in the context detection field. One important challenge in this area is automatically detecting the user’s task because once it is known it is possible to support her better. In this paper we propose an ontology-based user interaction context model (UICO) that enhances the performance of task detection on the user’s computer desktop. Starting from low-level contextual attention meta-data captured from the user’s desktop, we utilize rule-based, information extraction and machine learning approaches to automatically populate this user interaction context model. Furthermore we automatically derive relations between the model’s entities and automatically detect the user’s task. We present evaluation results of a large-scale user study we carried out in a knowledge-intensive business environment, which support our approach