Paper Info
Title
Measuring Knowledge Delivery Quantity of Associated Knowledge Flow
Abstract
Associated knowledge flow (AKF) is a sequential link between associated topics, which can be applied to intelligent browsing and personalized recommendation. One key problem is how to measure the knowledge delivery quantity (KDQ) on an AKF. In this paper, a computational method of knowledge delivery quantity on an AKF is proposed. Firstly, considering the keywords and associated relations between two nodes, four key factors for knowledge delivery quantity between two nodes are investigated. Secondly, based on the four factors, an algorithm is proposed to calculate the knowledge delivery quantity between two nodes. Thirdly, the knowledge delivery quantity of a node with adjacent nodes is calculated for the measurement of local knowledge delivery on an AKF. Lastly, according to the local knowledge delivery, the average knowledge delivery quantity is proposed to measure an AKF. Experimental results show that the proposed measurement method is accurate and effective.
Year
DOI
Venue
2008
10.1109/SKG.2008.92
SKG
Keywords
Field
DocType
knowledge discovery,data mining,data structures,boolean functions,local knowledge,knowledge engineering,thyristors
Boolean function,Data mining,Distance measurement,Data structure,Computer science,Knowledge extraction,Knowledge engineering,Knowledge flow
Conference
Volume
Issue
Citations 
null
null
2
PageRank 
References 
Authors
0.47
11
6
Name
Order
Citations
PageRank
Shunxiang Zhang112518.93
Xiangfeng Luo21251124.38
Jinjun Chen320.47
Zheng Xu435219.51
Jie Yu5226.88
Weimin Xu6617.98