1
Department of Computer Engineering, University of Guilan
2
Department of Electrical Engineering, University of Guilan
Abstract
A search engine is a kind of tool which fulfills users' information needs. Users try to obtain the desirable objectives adopting different queries in search engines. Some queries are carried out through inserting effective keywords. If a search engine fairly understands the relation among words, the users can certainly extract better results. However, understanding these relations and expressing the user's intention relatively refer to the structure of queries in any language. It is not easy to perform searchs in a Persian search engine according to the syntactic, pronunciation and spelling rules. In order to enhance the accuracy of the Persian search engines, the concept of ontology can be used to describe words and understanding their concepts. This study presented a model for improving the extraction of semantic relations from Persian language in terms of the performance of search engine and ontology. In this model, a standard Persian text collection named Bijankhan corpus is used. Persian language and syntactic patterns obtained from experiments on some texts of this corpus indicated that the accuracy of the proposed model is about 87%.
Darvishi, S., Shahbahrami, A., & Nahvi, M. (2014). The Extension of Persian Search Engine Based on Improving
Ontology Learning Process. Journal of Soft Computing and Information Technology, 3(3), 12-29.
MLA
Sima Darvishi; Asadollah Shahbahrami; Manouchehr Nahvi. "The Extension of Persian Search Engine Based on Improving
Ontology Learning Process". Journal of Soft Computing and Information Technology, 3, 3, 2014, 12-29.
HARVARD
Darvishi, S., Shahbahrami, A., Nahvi, M. (2014). 'The Extension of Persian Search Engine Based on Improving
Ontology Learning Process', Journal of Soft Computing and Information Technology, 3(3), pp. 12-29.
VANCOUVER
Darvishi, S., Shahbahrami, A., Nahvi, M. The Extension of Persian Search Engine Based on Improving
Ontology Learning Process. Journal of Soft Computing and Information Technology, 2014; 3(3): 12-29.