A Multi-Agent Neural Network System for Web Text Mining

Lean Yu; Shouyang Wang; Kin Keung Lai

doi:10.4018/978-1-59904-990-8.ch013

A Multi-Agent Neural Network System for Web Text Mining

Lean Yu
, Shouyang Wang
, Kin Keung Lai

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 12 - Chapter in an edited book (Author) › peer-review

1 Citation (Scopus)

Abstract

With the rapid increase of the huge amount of online information, there is a strong demand for Web text mining which helps people discover some useful knowledge from Web documents. For this purpose, this chapter first proposes a back-propagation neural network (BPNN)-based Web text mining system for decision support. In the BPNN-based Web text mining system, four main processes, Web document search, Web text processing, text feature conversion, and BPNN-based knowledge discovery, are involved. Particularly, BPNN is used as an intelligent learning agent that learns about underlying Web documents. In order to scale the individual intelligent agent with the large number of Web documents, we then provide a multi-agent-based neural network system for Web text mining in a parallel way. For illustration purpose, a simulated experiment is performed. Experiment results reveal that the proposed multi-agent neural network system is an effective solution to large scale Web text mining. © 2009, IGI Global.

Original language	English
Title of host publication	Handbook of Research on Text and Web Mining Technologies
Editors	Min Song, Yi-fang Brook Wu
Publisher	IGI Global Publishing
Chapter	XIII
Pages	201-225
Number of pages	25
Volume	I
ISBN (Electronic)	9781599049915
ISBN (Print)	9781599049908
DOIs	https://doi.org/10.4018/978-1-59904-990-8.ch013
Publication status	Published - 2009

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Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

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title = "A Multi-Agent Neural Network System for Web Text Mining",

abstract = "With the rapid increase of the huge amount of online information, there is a strong demand for Web text mining which helps people discover some useful knowledge from Web documents. For this purpose, this chapter first proposes a back-propagation neural network (BPNN)-based Web text mining system for decision support. In the BPNN-based Web text mining system, four main processes, Web document search, Web text processing, text feature conversion, and BPNN-based knowledge discovery, are involved. Particularly, BPNN is used as an intelligent learning agent that learns about underlying Web documents. In order to scale the individual intelligent agent with the large number of Web documents, we then provide a multi-agent-based neural network system for Web text mining in a parallel way. For illustration purpose, a simulated experiment is performed. Experiment results reveal that the proposed multi-agent neural network system is an effective solution to large scale Web text mining. {\textcopyright} 2009, IGI Global.",

author = "Lean Yu and Shouyang Wang and Lai, \{Kin Keung\}",

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year = "2009",

doi = "10.4018/978-1-59904-990-8.ch013",

language = "English",

isbn = "9781599049908",

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editor = "Min Song and Wu, \{Yi-fang Brook\}",

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A Multi-Agent Neural Network System for Web Text Mining. / Yu, Lean; Wang, Shouyang; Lai, Kin Keung.
Handbook of Research on Text and Web Mining Technologies. ed. / Min Song; Yi-fang Brook Wu. Vol. I IGI Global Publishing, 2009. p. 201-225.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 12 - Chapter in an edited book (Author) › peer-review

TY - CHAP

T1 - A Multi-Agent Neural Network System for Web Text Mining

AU - Yu, Lean

AU - Wang, Shouyang

AU - Lai, Kin Keung

N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

PY - 2009

Y1 - 2009

N2 - With the rapid increase of the huge amount of online information, there is a strong demand for Web text mining which helps people discover some useful knowledge from Web documents. For this purpose, this chapter first proposes a back-propagation neural network (BPNN)-based Web text mining system for decision support. In the BPNN-based Web text mining system, four main processes, Web document search, Web text processing, text feature conversion, and BPNN-based knowledge discovery, are involved. Particularly, BPNN is used as an intelligent learning agent that learns about underlying Web documents. In order to scale the individual intelligent agent with the large number of Web documents, we then provide a multi-agent-based neural network system for Web text mining in a parallel way. For illustration purpose, a simulated experiment is performed. Experiment results reveal that the proposed multi-agent neural network system is an effective solution to large scale Web text mining. © 2009, IGI Global.

AB - With the rapid increase of the huge amount of online information, there is a strong demand for Web text mining which helps people discover some useful knowledge from Web documents. For this purpose, this chapter first proposes a back-propagation neural network (BPNN)-based Web text mining system for decision support. In the BPNN-based Web text mining system, four main processes, Web document search, Web text processing, text feature conversion, and BPNN-based knowledge discovery, are involved. Particularly, BPNN is used as an intelligent learning agent that learns about underlying Web documents. In order to scale the individual intelligent agent with the large number of Web documents, we then provide a multi-agent-based neural network system for Web text mining in a parallel way. For illustration purpose, a simulated experiment is performed. Experiment results reveal that the proposed multi-agent neural network system is an effective solution to large scale Web text mining. © 2009, IGI Global.

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U2 - 10.4018/978-1-59904-990-8.ch013

DO - 10.4018/978-1-59904-990-8.ch013

M3 - RGC 12 - Chapter in an edited book (Author)

SN - 9781599049908

VL - I

SP - 201

EP - 225

BT - Handbook of Research on Text and Web Mining Technologies

A2 - Song, Min

A2 - Wu, Yi-fang Brook

PB - IGI Global Publishing

ER -

A Multi-Agent Neural Network System for Web Text Mining

Abstract

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