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Discovery of textual knowledge flow based on the management of knowledge maps
Author(s) -
Luo Xiangfeng,
Hu Qingliang,
Xu Weimin,
Yu Zhian
Publication year - 2008
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.1319
Subject(s) - relation (database) , computer science , grid , knowledge extraction , semantic web , hash function , information retrieval , domain knowledge , knowledge flow , world wide web , data mining , knowledge management , geography , computer security , geodesy
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.