Due to the increasing web, there are many challenges to establish a general framework for data mining and retrieving structured data from the Web. Creating an ontology is a step towards solving this problem. The ontology raises the main entity and the concept of any data in data mining. In this paper, we tried to propose a method for applying the "meaning" of the search system, But the problem for these methods is building a knowledge base that can be used for semantic search. The previous work interprets the query in three ways:'semantic relation in ontology', 'co-occurrence in the document', and 'semantic relation from Thesaurus'. The proposed method has two parts. The first part, using domain ontology for classified web pages based on keyword and the concept in each domain and builds Fuzzy ontology as Knowledge Base and the next section offers a method for expanding the query using built fuzzy ontology. In this paper, we tried to create knowledge base with WordNet as a comprehensive dictionary and extracted Sub string (phrases include multi words) from WordNet for each keyword in each domain ontology. The created Search engine was applied to an experimental system to evaluate the "precision – Recall” and it was revealed that applying the proposed method can improve query expansion 11%better in our experiments for precision.
Rabiee Kenari, S., Nazemi, E. (2012). Query Architecture Expansion in Web Using Fuzzy Multi Domain Ontology. Journal of Computer & Robotics, 5(1), 15-21.
MLA
Shaghayegh Rabiee Kenari; Eslam Nazemi. "Query Architecture Expansion in Web Using Fuzzy Multi Domain Ontology". Journal of Computer & Robotics, 5, 1, 2012, 15-21.
HARVARD
Rabiee Kenari, S., Nazemi, E. (2012). 'Query Architecture Expansion in Web Using Fuzzy Multi Domain Ontology', Journal of Computer & Robotics, 5(1), pp. 15-21.
VANCOUVER
Rabiee Kenari, S., Nazemi, E. Query Architecture Expansion in Web Using Fuzzy Multi Domain Ontology. Journal of Computer & Robotics, 2012; 5(1): 15-21.
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