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Iconic categorization with knowledge-based “icon systems” can improve collaborative KM

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Abstract

Icon system could represent an efficient solution for collective iconic categorization of knowledge by providing graphical interpretation. Their pictorial characters assist visualizing the structure of text to become more understandable beyond vocabulary obstacle. In this paper we are proposing a Knowledge Engineering (KM) based iconic representation approach. We assume that these systematic icons improve collective knowledge management. Meanwhile, text (constructed under our knowledge management model - Hypertopic) helps to reduce the diversity of graphical understanding belonging to different users. This “position paper” also prepares to demonstrate our hypothesis by an “iconic social tagging” experiment which is to be accomplished in 2011 with UTT students. We describe the “socio semantic web” information portal involved in this project, and a part of the icons already designed for this experiment in Sustainability field. We have reviewed existing theoretical works on icons from various origins, which can be used to lay the foundation of robust “icons systems”.
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hal-02918534 , version 1 (20-08-2020)

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Xiaoyue Ma, Jean-Pierre Cahier. Iconic categorization with knowledge-based “icon systems” can improve collaborative KM. 2011 International Conference on Collaboration Technologies and Systems (CTS), May 2011, Philadelphia, United States. pp.216-223, ⟨10.1109/CTS.2011.5928690⟩. ⟨hal-02918534⟩
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