Discovery of textual knowledge flow based on the management of knowledge maps

Authors:
Xiangfeng Luo;Qingliang Hu;Weimin Xu;Zhian Yu
Affiliations:
Digital Content Analysis and Semantic Grid Group, Key Laboratory of Grid Technology, Shanghai University, Shanghai 200072, China;Digital Content Analysis and Semantic Grid Group, Key Laboratory of Grid Technology, Shanghai University, Shanghai 200072, China;Digital Content Analysis and Semantic Grid Group, Key Laboratory of Grid Technology, Shanghai University, Shanghai 200072, China;Digital Content Analysis and Semantic Grid Group, Key Laboratory of Grid Technology, Shanghai University, Shanghai 200072, China
Venue:
Concurrency and Computation: Practice & Experience - 2nd International Workshop on Workflow Management and Applications in Grid Environments (WaGe2007)
Year:
2008

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Abstract

Textual knowledge flow (TKF) provides an effective technique and theoretical support for the discovery and cooperation of knowledge innovation, intelligent browsing, and personalized recommendation in Web services and e-Science Knowledge Grid. For the discovery of TKF, firstly knowledge map (KM) is proposed to represent the textual knowledge; then a hash algorithm is used to code KMs in order to form an Island which contains enormous KMs belonging to a domain. Under the control of the Island, C-Location and R-Location are introduced to manage those KMs belonging to an Island. KM-Chord is proposed to manage the number of Islands, C-Locations and R-Locations in Web or a library. With the help of the management of KMs, similar relation and associated relation between KMs are found to build the semantic link network (SLN) between KMs. Based on the SLN and users' profile and input, similar or associated TKF with the user's different demands is activated. Experiments show that the proposed method can effectively discover TKF for Web services and e-Science Knowledge Grid. Copyright © 2008 John Wiley & Sons, Ltd.