Open Taiwan Government data recommendation platform using DBpedia and Semantic Web based on cloud computing

Abstract

Purpose In recent years, governments around the world are actively promoting the Open Government Data (OGD) to facilitate reusing open data and developing information applications. Currently, there are more than 35,000 data sets available on the Taiwan OGD website. However, the existing Taiwan OGD website only provides keyword queries and lacks a friendly query interface. This study aims to address these issues by defining a DBpedia cloud computing framework (DCCF) for integrating DBpedia with Semantic Web technologies into Spark cluster cloud computing environment. Design/methodology/approach The proposed DCCF is used to develop a Taiwan OGD recommendation platform (TOGDRP) that provides a friendly query interface to automatically filter out the relevant data sets and visualize relationships between these data sets. Findings To demonstrate the feasibility of TOGDRP, the experimental results illustrate the efficiency of the different cloud computing models, including Hadoop YARN cluster model, Spark standalone cluster model and Spark YARN cluster model. Originality/value The novel solution proposed in this study is a hybrid approach for integrating Semantic Web technologies into Hadoop and Spark cloud computing environment to provide OGD data sets recommendation.

Recent Trends of Machine Learning Predictions Using Open Data: A Systematic Review

“…Over the years, the rise of publishingproducing countries could be aided by government memorandums (Gray, 2014;Wright, 2014) and also by the guidelines of OD principles (Nugroho et al, 2015), which are believed to encourage public knowledge openness and interoperability without barriers to its reuse and consumption. Taiwan debuted its first OD portal in 2013 as compared to the other governments (Chen & Hsu, 2019).…”