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Optimizing Search Results using Wikipedia based ESS and Enhanced TF-IDF Approach

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Amit Rajeshwarkar, Meghana Nagori

Amit Rajeshwarkar and Meghana Nagori. Optimizing Search Results using Wikipedia based ESS and Enhanced TF-IDF Approach. International Journal of Computer Applications 144(12):23-28, June 2016. BibTeX

	author = {Amit Rajeshwarkar and Meghana Nagori},
	title = {Optimizing Search Results using Wikipedia based ESS and Enhanced TF-IDF Approach},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2016},
	volume = {144},
	number = {12},
	month = {Jun},
	year = {2016},
	issn = {0975-8887},
	pages = {23-28},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2016910498},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The Triangular Search approach aims at recalculating authenticity of the Search Results provided by the Google API with the help of Semantic similarity provided by Wikipedia API and calculating the cosine similarity between the Document Vectors and query string vectors using enhanced approach of Tf-Idf that reduces calculation involved in it.

The Search Engine Optimization traces anchor texts that are the values between a tag of HTML and body texts of a web page. Using the Vector Space Model, the Term frequency and Inverse document frequency are calculated along with the Page ranking algorithm to get the Search Results. But consideration of anchor texts in search engine optimization techniques leads to some of the non-relevant body texts of a document. Also the top results of a search engine include trending and e-commerce links other than sponsored links but the intent of search is not considered.

This approach proposes and gains user intents behind the search thereby focusing on providing intent related search results.


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Google API, Wikipedia API, Explicit Semantic Analysis (ESA), Enhanced TF-IDF.