DocumentCode :
2226181
Title :
Context sensitive text mining and belief revision for adaptive information retrieval
Author :
Lau, Raymond Y K
Author_Institution :
Centre of Inf. Technol. Innovation, Queensland Univ. of Technol., Brisbane, Qld., Australia
fYear :
2003
fDate :
13-17 Oct. 2003
Firstpage :
256
Lastpage :
262
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 context sensitive text mining. We illustrate a novel approach of integrating the proposed text mining method into the belief revision based adaptive information agents to improve the agents´ learning autonomy and prediction power.
Keywords :
Internet; belief maintenance; data mining; information retrieval; software agents; Internet; adaptive information retrieval; autonomous information agent; belief revision logic; context sensitive text mining; contextual knowledge; user information preference; Australia; Databases; Electronic mail; Information retrieval; Information technology; Internet; Output feedback; Search engines; Technological innovation; Text mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Web Intelligence, 2003. WI 2003. Proceedings. IEEE/WIC International Conference on
Print_ISBN :
0-7695-1932-6
Type :
conf
DOI :
10.1109/WI.2003.1241202
Filename :
1241202
Link To Document :
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