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Publishing E-RDF Linked Data for Many Agents by Single Third-Party Server

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Semantic Technology (JIST 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10675))

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Abstract

Linked data is one of the most successful practices in semantic web, which has led to the opening and interlinking of data. Though many agents (mostly academic organizations and government) have published a large amount of linked data, numerous agents such as private companies and industries either do not have the ability or do not want to make an additional effort to publish linked data. Thus, for agents who are willing to open part of their data but do not want to make an effort, the task can be undertaken by a professional third-party server (together with professional experts) that publishes linked data for these agents. Consequently, when a single third-party server is on behalf of multiple agents, it is also responsible to organize these multiple-source URIs (data) in a systematic way to make them referable, satisfying the 4-star data principles, as well as protect the confidential data of these agents. In this paper, we propose a framework to leverage these challenges and design a URI standard based on our proposed E-RDF, which extends and optimizes the existing 5-star linked data principles. Also, we introduce a customized data filtering mechanism to protect the confidential data. For validation, we implement a prototype system as a third-party server that publishes linked data for a number of agents. It demonstrates well-organized 5-star linked data plus E-RDF and shows the additional advantages of data integration and interlinking among agents.

D. Wang and Y. Zhang contributed equally to the work and serve as co-first authors.

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Acknowledgments

This work was supported by Shanghai Institute of Life Sciences Information Center Foundation: Research Output Evaluation (π Index) Based on Linked Data and Knowledge Graph Analysis (2016–2020).

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Authors and Affiliations

  1. Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

    Dongsheng Wang

  2. Department of Library, Information and Archives, Shanghai University, Shanghai, China

    Yongyuan Zhang

  3. Shanghai Information Center for Life Sciences, Chinese Academy of Sciences, Beijing, China

    Yongyuan Zhang & Tao Chen

  4. Department of Chemical Physics, Lund University, Lund, Sweden

    Zhengjun Wang

Authors
  1. Dongsheng Wang
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  2. Yongyuan Zhang
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  3. Zhengjun Wang
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  4. Tao Chen
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Corresponding author

Correspondence to Tao Chen .

Editor information

Editors and Affiliations

  1. Griffith University, Brisbane, Queensland, Australia

    Zhe Wang

  2. Dresden University of Technology, Dresden, Germany

    Anni-Yasmin Turhan

  3. Griffith University, Brisbane, Queensland, Australia

    Kewen Wang

  4. Tianjin University, Tianjin, China

    Xiaowang Zhang

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Wang, D., Zhang, Y., Wang, Z., Chen, T. (2017). Publishing E-RDF Linked Data for Many Agents by Single Third-Party Server. In: Wang, Z., Turhan, AY., Wang, K., Zhang, X. (eds) Semantic Technology. JIST 2017. Lecture Notes in Computer Science(), vol 10675. Springer, Cham. https://doi.org/10.1007/978-3-319-70682-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-70682-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70681-8

  • Online ISBN: 978-3-319-70682-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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