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A general framework of smart Open Linked Government Data: Application in E-health

Published: 15 March 2019 Publication History
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  • Abstract

    The exploitation of information is deeply rooted in major Government functions such as service provisioning, inspection, and policy development. Open Government Data (OGD) initiatives provide mean for stakeholders to obtain government information about a locality or country, in order to reuse them and create a source of enrichment in several ways: new user services, internal lever of modernization, economic development and increased transparency. Various actors around the world are focusing on the availability of open public data in data portals, by applying legal guidelines and beneficiating from the technical competence of public organizations. While these open data government portals are offering tools to present, search, download and visualize the government information, critical voices start addressing some issues of availability of a large amount of replicated datasets, therefore, a difficulty of finding relevant datasets and accessibility of datasets without connection between them. In this paper a framework for generating smart open linked government data (smart OLGD) is proposed, this framework profits from several technologies, Linked data, Natural language processing to aggregate in a semantic level similar datasets and Ratings-Based Recommender Systems to pro-vide suggestions of datasets that may represent a potential interest for citizens.

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    • (2023)Methodologies for publishing linked open government data on the Web: A systematic mapping and a unified process modelSemantic Web10.3233/SW-22289614:3(585-610)Online publication date: 5-Apr-2023

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    1. A general framework of smart Open Linked Government Data: Application in E-health

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      cover image ACM Other conferences
      ICGDA '19: Proceedings of the 2019 2nd International Conference on Geoinformatics and Data Analysis
      March 2019
      156 pages
      ISBN:9781450362450
      DOI:10.1145/3318236
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • Department of Informatics, University of Oslo

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      Association for Computing Machinery

      New York, NY, United States

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      Published: 15 March 2019

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      Author Tags

      1. Data quality
      2. Dictionary based approach
      3. Health data
      4. Linked data
      5. Natural language processing
      6. Open government data
      7. Recommender systems

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      • (2023)Methodologies for publishing linked open government data on the Web: A systematic mapping and a unified process modelSemantic Web10.3233/SW-22289614:3(585-610)Online publication date: 5-Apr-2023

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