DocumentCode :
467747
Title :
On the Extraction and Matching Between Structural Expertise and Fuzzy-Based Description of Objects
Author :
Wang, Hsien-chang ; Yang, Pei-ching
Author_Institution :
Chang Jung Christian Univ., Tainan
Volume :
3
fYear :
2007
fDate :
19-22 Aug. 2007
Firstpage :
1399
Lastpage :
1402
Abstract :
The representation of object features is an important task in the researches such as pattern recognition, information retrieval, interactive query, etc. This paper addresses how to integrate computational linguistics and fuzzy set techniques to automatically establish the knowledge base for semi-structural domain expertise. The proposed task includes document pre-processing, lexical vector encoding, fuzzy vector shrinking and fuzzy membership encoding. The content of a wild bird illustrated book was used as the training corpus and the domain expertise was established. Queries from another book, an expert and a naive were used as testing corpus. The preliminary results showed that the proposed approach is suitable for representing domain expertise. The top-N scores an average of 79 query for a specific object.
Keywords :
computational linguistics; fuzzy set theory; knowledge representation; query processing; book queries; computational linguistics; document preprocessing; fuzzy membership encoding; fuzzy set techniques; fuzzy vector shrinking; fuzzy-based description; information retrieval; interactive query; knowledge base; knowledge representation; lexical vector encoding; object feature representation; pattern recognition; semistructural domain expertise; structural expertise; training corpus; wild bird illustrated book; Birds; Books; Computational linguistics; Cybernetics; Encoding; Fuzzy logic; Fuzzy sets; Knowledge representation; Machine learning; Testing; Domain expertise; Fuzzy set; Knowledge representation; Object feature;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
Type :
conf
DOI :
10.1109/ICMLC.2007.4370363
Filename :
4370363
Link To Document :
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