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2006 International Symposium on Applications and the Internet (SAINT'06)
A Mining Method for LinkedWeb Pages Using Associated Keyword Space
Phoenix, Arizona
January 23-January 27
ISBN: 0-7695-2508-3
ASCII Text
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Yuuichi Yaguchi, Hiroshi Ohnishi, Satoshi Mori, Keitaro Naruse, Ryuichi Oka, Hironobu Takahashi, "A Mining Method for LinkedWeb Pages Using Associated Keyword Space," 2012 IEEE/IPSJ 12th International Symposium on Applications and the Internet, pp. 268-276, 2006 International Symposium on Applications and the Internet (SAINT'06), 2006.
 
 
BibTex
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@article{ 10.1109/SAINT.2006.4,
author = {Yuuichi Yaguchi and Hiroshi Ohnishi and Satoshi Mori and Keitaro Naruse and Ryuichi Oka and Hironobu Takahashi},
title = {A Mining Method for LinkedWeb Pages Using Associated Keyword Space},
journal ={2012 IEEE/IPSJ 12th International Symposium on Applications and the Internet},
volume = {0},
year = {2006},
isbn = {0-7695-2508-3},
pages = {268-276},
doi = {http://doi.ieeecomputersociety.org/10.1109/SAINT.2006.4},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - 2012 IEEE/IPSJ 12th International Symposium on Applications and the Internet
TI - A Mining Method for LinkedWeb Pages Using Associated Keyword Space
SN - 0-7695-2508-3
SP268
EP276
A1 - Yuuichi Yaguchi,
A1 - Hiroshi Ohnishi,
A1 - Satoshi Mori,
A1 - Keitaro Naruse,
A1 - Ryuichi Oka,
A1 - Hironobu Takahashi,
PY - 2006
KW - null
VL - 0
JA - 2012 IEEE/IPSJ 12th International Symposium on Applications and the Internet
ER -
 
Yuuichi Yaguchi, University of Aizu
Hiroshi Ohnishi, University of Aizu
Satoshi Mori, University of Aizu
Keitaro Naruse, University of Aizu
Ryuichi Oka, University of Aizu
Hironobu Takahashi, Mediadrive Inc.
We propose a novel method for mining knowledge from linkedWeb pages. Unlike most conventional methods for extracting knowledge from linked data, which are based on graph theory, the proposed method is based on our Associated Keyword Space (ASKS), which is a nonlinear version of linear multidimensional scaling (MDS), such as Quantification Method Type IV (Q-IV). We constructed a three-dimensional ASKS space using linked HTML data from the World Wide Web. Experimental results confirm that the performance of ASKS is superior to that of Q-IV for discriminating clusters in the space obtained. We also demonstrate a mining procedure realized by 1) finding subspaces obtained in terms of logical calculations between subspaces in an ASKS space and 2) detecting emerging spatial patterns with geometrical features.
Citation:
Yuuichi Yaguchi, Hiroshi Ohnishi, Satoshi Mori, Keitaro Naruse, Ryuichi Oka, Hironobu Takahashi, "A Mining Method for LinkedWeb Pages Using Associated Keyword Space," saint, pp.268-276, 2006 International Symposium on Applications and the Internet (SAINT'06), 2006
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