Representation of context-dependant knowledge in ontologies: A model and an application
Most of current Information and Knowledge Based Systems manage impressive amounts of information, ranging from local databases to resources imported from the web. In addition to widely pointed-out integration and maintenance difficulties, other common problem is overwhelming of users with much more information than the strictly necessary for fulfilling a task, forcing them to dig in a list of results to find valuable answers. This issue is especially critical in mobile decision support systems, since neither the capabilities of the handheld devices nor the users situation are likely to ease or even permit carrying out this manual post-processing. Use of context knowledge has been envisioned as an appropriate solution to deal with this information overload matter: system responses can be summarized and customized depending on the situation and the preferences of the user, which results in presenting him only relevant information. In this work we propose a formal model for representing in ontologies relevance relations between context descriptions and domain-knowledge subsets. Besides the formulation of the model, we describe an algorithm to reason within it. We demonstrate the contributions of our approach with the implementation of the IASO application, a system which provide doctors in nomadic healthcare with brief context-dependant pieces of advice about patients electronic health records.
ontologies knowledge representation mobile systems context-aware systems medical informatics