Babalou, Samira, Kargar, Mohammad Javad, Davarpanah, Seyyed Hashem. (1394). Centralized Clustering Method To Increase Accuracy In Ontology Matching Systems. نشریه مهندسی عمران امیرکبیر, 45(2), 1-10. doi: 10.22060/miscj.2015.524
Samira Babalou; Mohammad Javad Kargar; Seyyed Hashem Davarpanah. "Centralized Clustering Method To Increase Accuracy In Ontology Matching Systems". نشریه مهندسی عمران امیرکبیر, 45, 2, 1394, 1-10. doi: 10.22060/miscj.2015.524
Babalou, Samira, Kargar, Mohammad Javad, Davarpanah, Seyyed Hashem. (1394). 'Centralized Clustering Method To Increase Accuracy In Ontology Matching Systems', نشریه مهندسی عمران امیرکبیر, 45(2), pp. 1-10. doi: 10.22060/miscj.2015.524
Babalou, Samira, Kargar, Mohammad Javad, Davarpanah, Seyyed Hashem. Centralized Clustering Method To Increase Accuracy In Ontology Matching Systems. نشریه مهندسی عمران امیرکبیر, 1394; 45(2): 1-10. doi: 10.22060/miscj.2015.524

Centralized Clustering Method To Increase Accuracy In Ontology Matching Systems

AUT Journal of Modeling and Simulation
مقاله 1، دوره 45، شماره 2، 2013، صفحه 1-10    اصل مقاله (803.99 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/miscj.2015.524
نویسندگان
Samira Babalou1؛ Mohammad Javad Kargar* 2؛ Seyyed Hashem Davarpanah2
1MSC student, Department of Computer Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran
2Assistant Professor, Department of Computer Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran
چکیده
Ontology is the main infrastructure of the Semantic Web which provides facilities for integration, searching and sharing of information on the web. Development of ontologies as the basis of semantic web and their heterogeneities have led to the existence of ontology matching. By emerging large-scale ontologies in real domain, the ontology matching systems faced with some problem like memory consumption. Therefore, partitioning the ontology was proposed. In this paper, a new clustering method for the concepts within ontologies is proposed, which is called SeeCC. The proposed method is a seeding-based clustering method which reduces the complexity of comparison by using clusters’ seed. The SeeCC method facilitates the memory consuming problem and increases their accuracy in the large-scale matching problem as well. According to the evaluation of SeeCC's results with Falcon-AO and the proposed system by Algergawy accuracy of the ontology matching is easily observed. Furthermore, compared to OAEI (Ontology Alignment Evaluation Initiative), SeeCC has acceptable result with the top ten systems.
کلیدواژه‌ها
Ontology matching؛ Clustering method؛ Large-scale matching؛ Semantic graph
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