Fine Grained Approach for Domain Specific Seed URL Extraction

Sanagavarapu, Lalit Mohan
Sarangi, Sourav
Y, Raghu Reddy
Varma, Vasudeva
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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.
Data Analytics, Data Mining and Machine Learning for Social Media, Domain,Fine Grained,Security,Seed URL,Sub-domain
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