Research on Multi-Source Data Integration Based on Ontology and Karma Modeling

Hongyan Yun, Ying He, Li Lin and Xiaohong Wang
Additional contact information
Hongyan Yun: College of Computer Science and Technology, Qingdao University, Qingdao, China
Ying He: School of Electronic Information, Qingdao University, Qingdao, China
Li Lin: College of Computer Science and Technology, Qingdao University, Qingdao, China
Xiaohong Wang: Qilu University of Technology, Shandong Academy of Science, Shandong Computer Science Center, Shandong, China

International Journal of Intelligent Information Technologies (IJIIT), 2019, vol. 15, issue 2, 69-87

Abstract: The purpose of data integration is that integrates multi-source heterogeneous data. Ontology solves semantic describing of multi-source heterogeneous data. The authors propose a practical approach based on ontology modeling and an information toolkit named Karma modeling for fast data integration, and demonstrate an application example in detail. Armed Conflict Location & Event Data Project (ACLED) is a publicly available conflict event dataset designed for disaggregated conflict analysis and crisis mapping. The authors analyzed the ACLED dataset and domain knowledge to build an Armed Conflict Event ontology, then constructed Karma models to integrate ACLED datasets and publish RDF data. Through SPARQL query to check the correctness of published RDF data. Authors design and developed an ACLED Query System based on Jena API, Canvas JS, and Baidu API, etc. technologies, which provides convenience for governments and researches to analyze regional conflict events and crisis early warning, and it verifies the validity of constructed ontology and the correctness of Karma modeling.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJIIT.2019040105 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jiit00:v:15:y:2019:i:2:p:69-87

Access Statistics for this article

International Journal of Intelligent Information Technologies (IJIIT) is currently edited by Vijayan Sugumaran

More articles in International Journal of Intelligent Information Technologies (IJIIT) from IGI Global
Bibliographic data for series maintained by Journal Editor ().