Fine Grained Approach for Domain Specific Seed URL Extraction

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dc.contributor.authorSanagavarapu, Lalit Mohan
dc.contributor.authorSarangi, Sourav
dc.contributor.authorY, Raghu Reddy
dc.contributor.authorVarma, Vasudeva
dc.date.accessioned2017-12-28T00:53:29Z
dc.date.available2017-12-28T00:53:29Z
dc.date.issued2018-01-03
dc.description.abstractDomain 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.
dc.format.extent8 pages
dc.identifier.doi10.24251/HICSS.2018.224
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/50111
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining and Machine Learning for Social Media
dc.subjectDomain,Fine Grained,Security,Seed URL,Sub-domain
dc.titleFine Grained Approach for Domain Specific Seed URL Extraction
dc.typeConference Paper
dc.type.dcmiText

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