Please wait a minute...
New Technology of Library and Information Service  2007, Vol. 2 Issue (3): 51-54    DOI: 10.11925/infotech.1003-3513.2007.03.11
Current Issue | Archive | Adv Search |
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)
Download: PDF (465 KB)  
Export: BibTeX | EndNote (RIS)      

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


Corresponding Authors: Zhang Xiaoluan     E-mail:
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:     OR

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

[1] Liu Ping,Peng Xiaofang. Calculating Word Similarities Based on Formal Concept Analysis[J]. 数据分析与知识发现, 2020, 4(5): 66-74.
[2] Jie Ma,Yan Ge,Hongyu Pu. Survey of Attribute Reduction Methods[J]. 数据分析与知识发现, 2020, 4(1): 40-50.
[3] Jiao Yan,Jing Ma,Kang Fang. Computing Text Semantic Similarity with Syntactic Network of Co-occurrence Distance[J]. 数据分析与知识发现, 2019, 3(12): 93-100.
[4] Liu Ping,Li Yanan,Yu Cong. Building Interactive Knowledge Map for Academic Search[J]. 数据分析与知识发现, 2018, 2(12): 43-51.
[5] Chen Erjing,Jiang Enbo. Review of Studies on Text Similarity Measures[J]. 数据分析与知识发现, 2017, 1(6): 1-11.
[6] Wang Zixuan,Le Xiaoqiu,He Yuanbiao. Recognizing Core Topic Sentences with Improved TextRank Algorithm Based on WMD Semantic Similarity[J]. 数据分析与知识发现, 2017, 1(4): 1-8.
[7] Zhai Dongsheng,Cai Wenhao,Zhang Jie,Li Zhenfei. An Improved Method of Semantic Similarity Calculation of Chinese Trademarks[J]. 数据分析与知识发现, 2017, 1(11): 19-28.
[8] Liu Jian,Bi Qiang,Liu Qingxu,Wang Fu. New Content Recommendation Service of Digital Literature[J]. 现代图书情报技术, 2016, 32(9): 70-77.
[9] Ba Zhichao,Li Gang,Zhu Shiwei. Similarity Measurement of Research Interests in Semantic Network[J]. 现代图书情报技术, 2016, 32(4): 81-90.
[10] Qiang Bi, Jian Liu, Yulai Bao. A New Text Clustering Method Based on Semantic Similarity[J]. 数据分析与知识发现, 2016, 32(12): 9-16.
[11] Liu Huailiang, Du Kun, Qin Chunxiu. Research on Chinese Text Categorization Based on Semantic Similarity of HowNet[J]. 现代图书情报技术, 2015, 31(2): 39-45.
[12] Fan Xuexue, Wang Zhirong, Xu Wu, Liang Yin, Ma Xiaohu. Research on Semantic Similarity Estimation Algorithm of Medical Terminology Based on Medical Ontology[J]. 现代图书情报技术, 2015, 31(12): 57-64.
[13] Yan Shiyan, Wang Shengqing, Luo Yunchuan, Huang Haojun. An Ontology Collaborative Construction Model Based on FCA in Cloud Computing Environment[J]. 现代图书情报技术, 2014, 30(3): 49-56.
[14] Hu Jiming, Xiao Lu. Semantic Incremental Improvement on Vector Space Model for Text Modeling[J]. 现代图书情报技术, 2014, 30(10): 49-55.
[15] He Chao, Zhang Yufeng. Research on Business Intelligence Link Analysis Algorithm Combining Semantic Similarity[J]. 现代图书情报技术, 2013, 29(3): 27-32.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938