EnSense-A commonality checker for Semantic Web

Salih Ismail, Talal Shaikh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The relative influence of Semantic Web on Artificial Intelligence and vice versa is ever increasing. However Artificial Intelligence and Semantic Web applications and research are going in different tangents as mentioned by the pioneers of Semantic Web. The real benefit would be on mixing semantic data for use in Artificially Intelligent apps so that reasoning can be done on open datasets for different entities. One way to do reasoning between entities would be by finding commonalities.That purpose we have created EnSense (Enhanced Sensing)-A web application that can find commonalities between two RDF objects using Semantic Web. We have devised an algorithm that could perform the task with greater speed, better number of relationships and quality as opposed to its closely related works. Our results have proven an 85% decrease in time taken and have 145% increase in the number of commonalities found. We provide a visual representation of the commonalities based on an interactive graph to the user.

Original languageEnglish
Title of host publication2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
PublisherIEEE
Pages430-435
Number of pages6
ISBN (Electronic)9781509032433
DOIs
Publication statusPublished - 5 Oct 2017

Keywords

  • Commonality
  • component
  • Relationship finder
  • Semantic Web

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Artificial Intelligence

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  • Cite this

    Ismail, S., & Shaikh, T. (2017). EnSense-A commonality checker for Semantic Web. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (pp. 430-435). IEEE. https://doi.org/10.1109/I-SMAC.2017.8058386