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Fine Grained Approach for Domain Specific Seed URL Extraction

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Title:Fine Grained Approach for Domain Specific Seed URL Extraction
Authors:Sanagavarapu, Lalit Mohan
Sarangi, Sourav
Y, Raghu Reddy
Varma, Vasudeva
Keywords:Data Analytics, Data Mining and Machine Learning for Social Media
Domain,Fine Grained,Security,Seed URL,Sub-domain
Date Issued:03 Jan 2018
Abstract:Domain Specific Search Engines are expected to provide relevant search results. Availability of enormous number of URLs across subdomains improves relevance of domain specific search engines. The current methods for seed URLs can be systematic ensuring representation of subdomains. We propose a fine grained approach for automatic extraction of seed URLs at subdomain level using Wikipedia and Twitter as repositories. A SeedRel metric and a Diversity Index for seed URL relevance are proposed to measure subdomain coverage. We implemented our approach for 'Security - Information and Cyber' domain and identified 34,007 Seed URLs and 400,726 URLs across subdomains. The measured Diversity index value of 2.10 conforms that all subdomains are represented, hence, a relevant 'Security Search Engine' can be built. Our approach also extracted more URLs (seed and child) as compared to existing approaches for URL extraction.
Pages/Duration:8 pages
URI/DOI:http://hdl.handle.net/10125/50111
ISBN:978-0-9981331-1-9
DOI:10.24251/HICSS.2018.224
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data Analytics, Data Mining and Machine Learning for Social Media


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