Sequential pattern mining and belief revision for adaptive information retrieval

Raymond Y. K. Lau; Yuefeng Li

doi:10.1109/AMT.2005.1505427

Sequential pattern mining and belief revision for adaptive information retrieval

Department of Information Systems

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

Autonomous information agents alleviate the information overload problem on the Internet. The AGM belief revision framework provides a rigorous foundation to develop adaptive information agents. The expressive power of the belief revision logic allow a user's information preferences and contextual knowledge of a retrieval situation to be captured and reasoned about within a single logical framework. Contextual knowledge for information retrieval can be acquired via sequential pattern mining. This paper illustrates a novel approach of integrating the proposed data mining method into the belief revision based adaptive information agents to improve the agents' learning autonomy and prediction power. © 2005 IEEE.

Original language	English
Title of host publication	Proceedings of the 2005 International Conference on Active Media Technology, AMT 2005
Pages	585-590
Volume	2005
DOIs	https://doi.org/10.1109/AMT.2005.1505427
Publication status	Published - 2005
Event	2005 International Conference on Active Media Technology, AMT 2005 - Kagawa, Japan Duration: 19 May 2005 → 21 May 2005

Publication series

Name
Volume	2005

Conference

Conference	2005 International Conference on Active Media Technology, AMT 2005
Place	Japan
City	Kagawa
Period	19/05/05 → 21/05/05

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title = "Sequential pattern mining and belief revision for adaptive information retrieval",

abstract = "Autonomous information agents alleviate the information overload problem on the Internet. The AGM belief revision framework provides a rigorous foundation to develop adaptive information agents. The expressive power of the belief revision logic allow a user's information preferences and contextual knowledge of a retrieval situation to be captured and reasoned about within a single logical framework. Contextual knowledge for information retrieval can be acquired via sequential pattern mining. This paper illustrates a novel approach of integrating the proposed data mining method into the belief revision based adaptive information agents to improve the agents' learning autonomy and prediction power. {\textcopyright} 2005 IEEE.",

author = "Lau, {Raymond Y. K.} and Yuefeng Li",

year = "2005",

doi = "10.1109/AMT.2005.1505427",

language = "English",

isbn = "0780390350",

volume = "2005",

pages = "585--590",

booktitle = "Proceedings of the 2005 International Conference on Active Media Technology, AMT 2005",

note = "2005 International Conference on Active Media Technology, AMT 2005 ; Conference date: 19-05-2005 Through 21-05-2005",

}

Sequential pattern mining and belief revision for adaptive information retrieval. / Lau, Raymond Y. K.; Li, Yuefeng.
Proceedings of the 2005 International Conference on Active Media Technology, AMT 2005. Vol. 2005 2005. p. 585-590 1505427.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

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AU - Li, Yuefeng

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N2 - Autonomous information agents alleviate the information overload problem on the Internet. The AGM belief revision framework provides a rigorous foundation to develop adaptive information agents. The expressive power of the belief revision logic allow a user's information preferences and contextual knowledge of a retrieval situation to be captured and reasoned about within a single logical framework. Contextual knowledge for information retrieval can be acquired via sequential pattern mining. This paper illustrates a novel approach of integrating the proposed data mining method into the belief revision based adaptive information agents to improve the agents' learning autonomy and prediction power. © 2005 IEEE.

AB - Autonomous information agents alleviate the information overload problem on the Internet. The AGM belief revision framework provides a rigorous foundation to develop adaptive information agents. The expressive power of the belief revision logic allow a user's information preferences and contextual knowledge of a retrieval situation to be captured and reasoned about within a single logical framework. Contextual knowledge for information retrieval can be acquired via sequential pattern mining. This paper illustrates a novel approach of integrating the proposed data mining method into the belief revision based adaptive information agents to improve the agents' learning autonomy and prediction power. © 2005 IEEE.

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U2 - 10.1109/AMT.2005.1505427

DO - 10.1109/AMT.2005.1505427

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 0780390350

SN - 9780780390355

VL - 2005

SP - 585

EP - 590

BT - Proceedings of the 2005 International Conference on Active Media Technology, AMT 2005

T2 - 2005 International Conference on Active Media Technology, AMT 2005

Y2 - 19 May 2005 through 21 May 2005

ER -

Sequential pattern mining and belief revision for adaptive information retrieval

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