Sachi Arafat

Work place: Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia



Research Interests: Computer systems and computational processes, Computer Architecture and Organization, Information Retrieval, Data Structures and Algorithms


Sachi Arafat is an Assistant Professor at the Department of Information Systems, King Abdulaziz University, Jeddah, Saudi Arabia. He was previously a Royal Academy of Engineering Postdoctoral Fellow at the School of Computer Science at the University of Glasgow, where he also completed his undergraduate degree and Ph.D. His work lay in the intersection of Information Science, Philosophy of Technology, Information Retrieval and Data Science. The most representative of this intersection is his book on ‘Search Foundations’ published in 2019 by the MIT Press.

Author Articles
Leveraging the Saudi Linked Open Government Data: A Framework and Potential Benefits

By Afnan M. AlSukhayri Muhammad Ahtisham Aslam Sachi Arafat Naif Radi Aljohani

DOI:, Pub. Date: 8 Jul. 2019

Open data initiatives are a crucial aspect of effective e-governance strategy. They embody aspirations towards sociopolitical values of transparency, trust, confidence, and accountability, pertaining to the relationship between a government and its citizens. The importance of such initiatives is especially important for an emerging economy such as Saudi Arabia which is undergoing rapid social changes directed by a contemporary national vision. The effectiveness of open data initiatives depends strongly on (a) the quality of the data available, (b) the soundness of the methodologies and suitability of platforms used to prepare and present the data, and (c) the ability of the data to facilitate the kinds of insights and social-action that are sought from that data to ensure successful e-governance. This paper investigates the feasibility of current Saudi government open data initiatives in this regard. It assesses existing approaches to improve the effectiveness of open government data through transforming it into linked-open data (using the Resource Description Framework [RDF]) by connecting disparate sources of structured data therein. It proposes to improve existing approaches by suggesting a framework for automating the linking sub-process of existing approaches and organizing the data to be queried through SPARQL. Moreover, it evaluates the potential benefit of this proposal by discussing the kinds of policy insights this could generate which would be difficult without it.

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