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RDF Dataset Management Framework for Data.go.th

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Recent Advances and Future Prospects in Knowledge, Information and Creativity Support Systems (KICSS 2015)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 685))

Abstract

Recently, Thailand has started initiating the Thailand open government data project that continuously triggers an increment in the number of open datasets. Open data is valuable when the data is reused, shared and integrated. Converting the existing datasets to the RDF format can increase the values of these datasets. In this paper, we present the architecture and processes for RDF dataset management for Data.go.th based on OAM Framework which supports the entire processes: RDF data publishing, and data querying. Our approach is different from other LOD platforms in that users do not require the knowledge of RDF and SPARQL. Our platform would facilitate data publishing and querying process for novice users and make it easier to use. This framework provides a common ontology-based search interface and RESTFul APIs constructed automatically for the datasets when they are published. With the provided services for the datasets, it can simplify the user’s tasks in publishing datasets and create applications for the datasets. In consuming the RDF data, we implemented a sample mash-up application which accessed the published weather and reservoir datasets from the Data.go.th website via RESTful APIs.

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Notes

  1. 1.

    Csv2rdf4lod (http://data-gov.tw.rpi.edu/wiki/Csv2rdf4lod).

  2. 2.

    OpenLink Virtuoso (http://virtuoso.openlinksw.com/).

  3. 3.

    Grafter (http://grafter.org/).

  4. 4.

    Information Workbench (http://www.fluidops.com/en/portfolio/information_workbench/).

  5. 5.

    OpenRefine (http://openrefine.org/).

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Acknowledgments

This project was funded by the Electronic Government Agency (EGA) and the National Science and Technology Development Agency (NSTDA), Thailand.

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Authors and Affiliations

  1. Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center (NECTEC), Pathum Thani, Thailand

    Pattama Krataithong, Marut Buranarach & Thepchai Supnithi

  2. Faculty of Science and Technology, Department of Computer Science, Thammasat University, Pathum Thani, Thailand

    Pattama Krataithong

Authors
  1. Pattama Krataithong
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  2. Marut Buranarach
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  3. Thepchai Supnithi
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Corresponding author

Correspondence to Pattama Krataithong .

Editor information

Editors and Affiliations

  1. School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand

    Thanaruk Theeramunkong

  2. Department of Automatic Control and Biomedical Engineering, AGH University of Science and Technology, Kraków, Poland

    Andrzej M.J. Skulimowski

  3. Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi-shi, Ishikawa, Japan

    Takaya Yuizono

  4. School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi-shi, Ishikawa, Japan

    Susumu Kunifuji

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Krataithong, P., Buranarach, M., Supnithi, T. (2018). RDF Dataset Management Framework for Data.go.th. In: Theeramunkong, T., Skulimowski, A., Yuizono, T., Kunifuji, S. (eds) Recent Advances and Future Prospects in Knowledge, Information and Creativity Support Systems. KICSS 2015. Advances in Intelligent Systems and Computing, vol 685. Springer, Cham. https://doi.org/10.1007/978-3-319-70019-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-70019-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70018-2

  • Online ISBN: 978-3-319-70019-9

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