Ana Maguitman
David Leake
ABSTRACT
Much intelligent user interfaces research addresses the problem of providing information relevant to a current user topic. However, little work addresses the complementary question of helping the user identify potential topics to explore next. In knowledge acquisition, this question is crucial to deciding how to extend previously-captured knowledge. This paper examines requirements for effective topic suggestion and presents a domain-independent topic-generation algorithm designed to generate candidate topics that are novel but related to the current context. The algorithm iteratively performs a cycle of topic formation, Web search for connected material, and context-based filtering. An experimental study shows that this approach significantly outperforms a baseline at developing new topics similar to those chosen by an expert for a hand-coded knowledge model.
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Index Terms
- Suggesting novel but related topics: towards context-based support for knowledge model extension
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Reviews
Maurice D Mulvenna
The research area of knowledge acquisition sounds like a cut-and-dried, clear academic field. However, this gentle sounding name masks a difficult process, that of transferring knowledge from our minds into a form that can be used within a computer representation, for sharing and reuse. This paper builds on the idea of concept mapping, which is increasingly being used in education and business to help users share and understand knowledge concepts. The ideas put forward here provide extensions to a concept mapping tool called CmapTool. The basic concept mapping tools have simple interfaces to "draw" topics, indicate relationships using arcs, and provide subsequent browsing and sharing capabilities. The extensions give the concept mapping tool the means to provide automatic suggestions of new concepts. The rationale for the work is sound. As people use concept mapping tools, they tend to stop and reflect upon the topics and the arc of concept maps. Sometimes, they need to carry out some desk research, or use a search engine to verify some information. The research described in this paper effectively augments this reflection process, by providing topics automatically. The paper is interesting, in that there is a clear desire to devise a domain-independent solution at the design stage. This is reflected in the approach taken, which uses a base topic list to seed Google searches, which are then filtered through an innovative clustering algorithm, based on co-clustering using terms and documents. Given the tremendous amount of research into technologies that support the semantic Web, it is quite refreshing to see that the resulting concept map representation is not intended to be used for automated reasoning applications; rather, it works by attempting to jog the user's memory during the modeling and reflection process in concept map generation. I look forward to seeing this technology incorporated into the CmapTool in the future. Online Computing Reviews Service
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