Meta Search Engine using Semantic Similarity and Correlation Coefficient
Naresh Kumar1, Deepak Sharma2, Nripendra Narayan Das3

1Naresh Kumar, Associate Professor, Department of CSE, Maharaja Surajmal Institute of Technology, New Delhi, India.
2Deepak Sharma, Assistant Professor, Department of IT, Jagannath International Management School, Vasant Kunj, New Delhi, India.
3Nripendra Narayan Das, Associate Professor, Department of Information Technology, Manipal University, Jaipur, India.
Manuscript received on June 15, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 10, 2020. | PP: 643-647 | Volume-9 Issue-9, July 2020 | Retrieval Number: 100.1/ijitee.I7261079920 | DOI: 10.35940/ijitee.I7261.079920
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Abstract: This paper aims to provide an intelligent way to query and rank the results of a Meta Search Engine. A Meta Search Engine takes input from the user and produces results which are gathered from other search engines. The main advantage of a Meta Search Engine over methodical search engine is its ability to extend the search space and allows more resources for the user. The semantic intelligent queries will be fetching the results from different search engines and the responses will be fed into our ranking algorithm. Ranking of the search results is the other important aspect of Meta search engines. When a user searches a query, there are number of results retrieved from different search engines, but only several results are relevant to user’s interest and others are not much relevant. Hence, it is important to rank results according to the relevancy with user query. The proposed paper uses intelligent query and ranking algorithms in order to provide intelligent meta search engine with semantic understanding. 
Keywords:  Meta Search Engine, Scrapping unit, Intelligent Query System, Rankings.
Scope of the Article: Semantic Web