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New Technology of Library and Information Service  2007, Vol. 2 Issue (3): 51-54    DOI: 10.11925/infotech.1003-3513.2007.03.11
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Computation of the Concept Semantic Similarity in FCA
Zhang Xiaoluan   Wang Xifeng
(Department of Computer Science, Baoji College of  Arts and Science, Baoji 721007, China)
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Abstract  

Both Formal Concept Analysis (FCA) and domain Ontologies are two kinds of knowledge representations formalisms and their aims are at modeling concepts. This paper proposes a method to compute the similarity between concepts in FCA. The experimental result shows this method is effective for concept similarity computation.

Key wordsFormal concept analysis      Domain Ontologies      Semantic similarity     
Received: 15 January 2007      Published: 25 March 2007
: 

TP391

 
Corresponding Authors: Zhang Xiaoluan     E-mail: bjwlxyzxl@163.com
About author:: Zhang Xiaoluan,Wang Xifeng

Cite this article:

Zhang Xiaoluan,Wang Xifeng . Computation of the Concept Semantic Similarity in FCA. New Technology of Library and Information Service, 2007, 2(3): 51-54.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2007.03.11     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2007/V2/I3/51

徐德智,郑春卉等. 基于SUMO的概念语义相似度研究. 计算机应用,2006, 26(1): 180-183
2Ganter B, Wille R. Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg, 1999
3Borst W N. Construction of Engineering Ontologies for Knowledge Sharing and Reuse. PhD thesis, University of Twente, Enschede, 1997
4Galil Z. Efficient Algorithms for Finding Maximum Matching in Graphs. ACM Computing Surveys, 1986 (18): 23-38

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