Title
Technical Papers Parallel Session-V: A framework for focused linked data crawler using context graphs
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.
Keywords
Location
C-10, AMAN CED
Session Theme
Technical Papers Parallel Session-V (Information Retrieval)
Session Type
Parallel Technical Session
Start Date
13-12-2015 4:10 PM
End Date
13-12-2015 4:30 PM
Recommended Citation
Bai, S., Hussain, S., & Khoja, S. (2015). Technical Papers Parallel Session-V: A framework for focused linked data crawler using context graphs. International Conference on Information and Communication Technologies. Retrieved from https://ir.iba.edu.pk/icict/2015/2015/29
COinS
Technical Papers Parallel Session-V: A framework for focused linked data crawler using context graphs
C-10, AMAN CED
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.
