Schema Matching Based on Weighted Fuzzy Concept Lattice

Author

Feng, Wang ; Xiaoping, Li ; Qian, Wang

Author_Institution

Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing

Volume

4

fYear

2008

fDate

12-14 Dec. 2008

Firstpage

508

Lastpage

511

Abstract

This paper introduces a new schema matching approach based on weighted fuzzy concept lattice. The procedure contains three steps. Firstly, we increase the evidence about each element being matched by applying naive Bayes classifier to classify the names and descriptions of the elements. Secondly, we use weighted fuzzy concept lattice to integrate the classified results as well as type messages and constrains. At last, a structural similarity measure is introduced to calculate the final matching. We present experimental results that demonstrate WFCL-based matching outperforms direct matching (without the benefit of WFCL).

Keywords

Bayes methods; fuzzy set theory; pattern classification; pattern matching; description classification; naive Bayes classifier; name classification; schema matching approach; structural similarity measure; weighted fuzzy concept lattice; Computer networks; Computer science; Computer science education; Data analysis; Databases; Dictionaries; Laboratories; Large-scale systems; Lattices; Software engineering; formal concept analysis; naive bayes classifier; schema matching; similarity measure; weighted fuzzy concept lattice;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Software Engineering, 2008 International Conference on

Conference_Location

Wuhan, Hubei

Print_ISBN

978-0-7695-3336-0

Type

conf

DOI

10.1109/CSSE.2008.566

Filename

4722669