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Resembling the desktop of personal computer in which files and applications are stored, MICE3desktop is a Web-based information desktop where individual can keep and manage resources such as links to Web sites and services, as well as Web sources and documents. Resources on the desktop are represented in Web directory structure and annotated with information such as concepts, entities, etc. Categories are used to organize resources in the directory. Each category is annotated with concept learnt from the contents of the category. MICE3desktop allows users to exploit information and knowledge from their resources. Users can retrieve information by navigating the directory structure, look up the resource index or searching the resource contents. The desktop also allows users to effectively manage their resources by enabling them to enhance categories with concept. The concept is further used to assist user in organizing resources into their directory

Categories are used to organize information and knowledge in directory system, folder etc. As the amount of information increase and the types of information diversify, it is common to have more categories created. As the number of categories increases, it becomes more difficult to organize, manage and look up information from existing categories. In this paper, categories are annotated with concept features to facilitate the access, retrieval and sharing of information in the categories. We have observed that training texts is crucial in learning the concept of a category and serves as a good measure to help human to construct the category model. Hence, we present a study on training texts selection and evaluate the effectiveness of training texts, as well as its capability to complement human's knowledge in constructing the category model. Experimental evaluation shows that using training texts approach in category model construction gives promising results in both effectiveness and complement measures

Having broad coverage of search results returned by various search sources, combining and organizing these results in a meaningful way has become a common issue in the field of information retrieval. In this demo paper, we describe our meta search system, MICE, that is able to aggregate and classify search results based on user-customized categories. Categories help user to focus on search results, with respect to the categories concept customized by the user.