A framework for focused linked data crawler using context graphs

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Document Type

Conference Paper

Publication Date

1-1-2016

Author Affiliation

  • Samita Bai is PhD Scholar at the Department of Computer Science, Institute of Business Administration, Karachi
  • Sharaf Hussain is Labs Administrator at Institute of Business Administration, Karachi
  • Shakeel Ahmed Khoja is Professor at Institute of Business Administration, Karachi

Conference Name

2015 International Conference on Information and Communication Technologies (ICICT)

Conference Location

Karachi, Pakistan

Conference Dates

12-13 December 2015

ISBN/ISSN

84973869972 (Scopus)

First Page

1

Last Page

6

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Abstract / Description

In this paper, we propose a framework for focused Linked Data (LD) crawler based on context graphs. A focused crawler searches for a specific subset of web, in our case it targets interlinked RDF data stores. The proposed crawler constructs set of context graphs for the given seed URIs by back crawling the web, and classifiers are trained to detect and assign documents to different categories based on the content type. These classifier help crawler in search and updating of context graphs automatically. The crawler are trained using supervised learning. Additionally, an extensive overview of existing LD crawlers is also provided along with its basic requirements, architecture, issues and challenges.

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