Semantically Enabled Content Convergence System for Large Scale RDF Big Data

Yongju Lee, Hongzhou Duan, Yuxiang Sun

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The growing number of large scale RDF Big Data raises a challenging data management problem; how should RDF Big Data be stored, queried and integrated. We propose a novel semantic-based content convergence system which consists of acquisition, RDF storage, ontology learning and mashup subsystems. This system serves as a basis for implementing other more sophisticated applications required in the area of Linked Big Data.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023
EditorsHossain Shahriar, Yuuichi Teranishi, Alfredo Cuzzocrea, Moushumi Sharmin, Dave Towey, AKM Jahangir Alam Majumder, Hiroki Kashiwazaki, Ji-Jiang Yang, Michiharu Takemoto, Nazmus Sakib, Ryohei Banno, Sheikh Iqbal Ahamed
PublisherIEEE Computer Society
Pages1019-1020
Number of pages2
ISBN (Electronic)9798350326970
DOIs
StatePublished - 2023
Event47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023 - Hybrid, Torino, Italy
Duration: 26 Jun 202330 Jun 2023

Publication series

NameProceedings - International Computer Software and Applications Conference
Volume2023-June
ISSN (Print)0730-3157

Conference

Conference47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023
Country/TerritoryItaly
CityHybrid, Torino
Period26/06/2330/06/23

Keywords

  • Big Data
  • convergence system
  • entity matching
  • mashup
  • ontology learning
  • storage system

Fingerprint

Dive into the research topics of 'Semantically Enabled Content Convergence System for Large Scale RDF Big Data'. Together they form a unique fingerprint.

Cite this