DocumentCode
3093517
Title
Notice of Violation of IEEE Publication Principles
An improved ant colony optimization for ontology matching
Author
Wang, Xiaoyun ; Xu, Qiyu
Author_Institution
Inst. of Manage. Sci. & Inf. Eng., Hangzhou Dianzi Univ., Hangzhou, China
Volume
4
fYear
2011
fDate
11-13 March 2011
Firstpage
234
Lastpage
238
Abstract
Notice of Violation of IEEE Publication Principles
"An Improved Ant Colony Optimization for Ontology Matching"
by Xiaoyun Wang, Qiyu Xu
in 3rd International Conference on Computer Research and Development (ICCRD), Vol.4, March 2011, pp.234-238
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Discrete Particle Swarm Optimization for Ontology Alignment"
by Jurgen Bock, Jan Hettenhausen
in Information Sciences, Vol. 192, August 15, 2010
The Hungarian algorithm used in ontology matching sometimes cannot get the solution since this algorithm does not converge when dealing with special data. In order to solve this problem, this paper presents an improved ant colony optimization for ontology matching problem (ACOM). We utilize many kinds of rating functions which are also called base matchers to evaluate the distance of two ontology entries. After getting the distance matrix of ontology entries, we use an improved ant colony optimization algorithm to extract the best alignment instead of using the traditional Hungarian method. Finally, a set of experiments are conducted to analysis and evaluate the performance of ACOM in solving ontology matching problem. We use Ontology Alignment Evaluation Initiative (OAEI) benchmark test suite with pairs of ontologies as test cases and compare our results with three algorithms presented in the OAEI 2008. The result- of experiments show that our system has a good performance in the ontology matching process.
"An Improved Ant Colony Optimization for Ontology Matching"
by Xiaoyun Wang, Qiyu Xu
in 3rd International Conference on Computer Research and Development (ICCRD), Vol.4, March 2011, pp.234-238
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Discrete Particle Swarm Optimization for Ontology Alignment"
by Jurgen Bock, Jan Hettenhausen
in Information Sciences, Vol. 192, August 15, 2010
The Hungarian algorithm used in ontology matching sometimes cannot get the solution since this algorithm does not converge when dealing with special data. In order to solve this problem, this paper presents an improved ant colony optimization for ontology matching problem (ACOM). We utilize many kinds of rating functions which are also called base matchers to evaluate the distance of two ontology entries. After getting the distance matrix of ontology entries, we use an improved ant colony optimization algorithm to extract the best alignment instead of using the traditional Hungarian method. Finally, a set of experiments are conducted to analysis and evaluate the performance of ACOM in solving ontology matching problem. We use Ontology Alignment Evaluation Initiative (OAEI) benchmark test suite with pairs of ontologies as test cases and compare our results with three algorithms presented in the OAEI 2008. The result- of experiments show that our system has a good performance in the ontology matching process.
Keywords
ontologies (artificial intelligence); optimisation; semantic Web; Hungarian algorithm; base matchers; improved ant colony optimization; ontology alignment evaluation initiative benchmark test suite; ontology matching problem; Algorithm design and analysis; Ant colony optimization; Benchmark testing; Decision support systems; Ontologies; Optimization; Pragmatics; ant colony algorithm; ontology matching; semantic web;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Research and Development (ICCRD), 2011 3rd International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-61284-839-6
Type
conf
DOI
10.1109/ICCRD.2011.5763903
Filename
5763903
Link To Document