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Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning
June 2009 (vol. 21 no. 6)
pp. 785-799
ASCII Text
x 
 
Hai Zhuge, "Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 6, pp. 785-799, June, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2008.141,
author = {Hai Zhuge},
title = {Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {21},
number = {6},
issn = {1041-4347},
year = {2009},
pages = {785-799},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.141},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning
IS - 6
SN - 1041-4347
SP785
EP799
EPD - 785-799
A1 - Hai Zhuge,
PY - 2009
KW - Community discovery
KW - e-learning
KW - emerging semantics
KW - semantic community
KW - Semantic Link Network.
VL - 21
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Hai Zhuge, Institute of Computing Technology, Chinese Academy of Sciences, Beijing
The World Wide Web provides plentiful contents for Web-based learning, but its hyperlink-based architecture connects Web resources for browsing freely rather than for effective learning. To support effective learning, an e-learning system should be able to discover and make use of the semantic communities and the emerging semantic relations in a dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper first suggests the Semantic Link Network (SLN), a loosely coupled semantic data model that can semantically link resources and derive out implicit semantic links according to a set of relational reasoning rules. By studying the intrinsic relationship between semantic communities and the semantic space of SLN, approaches to discovering reasoning-constraint, rule-constraint, and classification-constraint semantic communities are proposed. Further, the approaches, principles, and strategies for discovering emerging semantics in dynamic SLNs are studied. The basic laws of the semantic link network motion are revealed for the first time. An e-learning environment incorporating the proposed approaches, principles, and strategies to support effective discovery and learning is suggested.

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Index Terms:
Community discovery, e-learning, emerging semantics, semantic community, Semantic Link Network.
Citation:
Hai Zhuge, "Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 6, pp. 785-799, June 2009, doi:10.1109/TKDE.2008.141
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