DocumentCode :
2539857
Title :
Text knowledge representation model based on human concept learning
Author :
Luo, Xiangfeng ; Cai, Chuanliang ; Hu, Qingliang
Author_Institution :
Sch. of Comput. Eng. & Sci., Shanghai Univ., Shanghai, China
fYear :
2010
fDate :
7-9 July 2010
Firstpage :
383
Lastpage :
390
Abstract :
The essential abilities of text knowledge representation, such as automatic construction, carrying abundant semantics and flexible reasoning, should be held due to the rapid growth of web resources and the requirements of the reasoning-based web services. However, current text knowledge representation models either lose many textual semantics or cannot be constructed automatically. To solve the above issues, text knowledge representation model is proposed based on the concept algebra of human concept learning. Then, the degree-2 power series hypothesis is developed and the reasoning ability of text representation is proposed. Finally, the results compared with current knowledge representation models show that our model performs better than other models in representing text knowledge.
Keywords :
Web services; algebra; inference mechanisms; knowledge representation; text analysis; Web resources; automatic construction; concept algebra; degree-2 power series hypothesis; human concept learning; reasoning-based Web services; text knowledge representation model; textual semantics; Algebra; Analytical models; Association rules; Cognition; Humans; Knowledge representation; Semantics; human concept learning; machine understanding; power series; text representation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8041-8
Type :
conf
DOI :
10.1109/COGINF.2010.5599710
Filename :
5599710
Link To Document :
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