Integrated machine learning with semantic web for open government data recommendation based on cloud computing. (14th September 2020)

• Record Type: Journal Article
• Authors: Hsu, I‐Ching; Lin, Yin‐Hung
• Is Part Of: Software, practice & experience. Volume 50:Number 12(2020)
• Page Start: 2293
• Page End: 2312
• Publication Date: 2020-09-14
• Subjects: cloud computing -- machine learning -- open government data -- semantic web; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2892
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Open government data (OGD) is a type of trusted information that can be used to verify the correctness of information on social platforms. Finding interesting OGD to serve personalized needs to facilitate the development of social platforms is a challenging research topic. This study explores how to link the Taiwanese government's open data platform with Facebook and how to recommend related OGD. First, an integrated machine learning with semantic web into cloud computing framework is defined. Next, the linked data query platform (LDQP) is developed to validate its feasibility. The LDQP provides a graphical approach for personal query and links with related Facebook fan pages. LDQP automatically finds highly relevant OGD based on recent topics that users are following on Facebook when users login to Facebook via the LDQP. In this way, the LDQP query result can be dynamically adjusted and graphically displayed according to user's Facebook operations.