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RDF Graph Visualization by Interpreting Linked Data as Knowledge

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Semantic Technology (JIST 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9544))

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Abstract

It is known that Semantic Web and Linked Open Data (LOD) are powerful technologies for knowledge management, and explicit knowledge is expected to be presented by RDF format (Resource Description Framework), but normal users are far from RDF due to technical skills required. As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be familiar with Semantic technology. However, an RDF graph generated from the whole query result is not suitable for reading, because it is highly connected like a hairball and less organized. To make a graph presenting knowledge be more proper to read, this research introduces an approach to sparsify a graph using the combination of three main functions: graph simplification, triple ranking, and property selection. These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users. A prototype is implemented to demonstrate the suitability and feasibility of the approach. It shows that the simple and flexible graph visualization is easy to read, and it creates the impression of users. In addition, the attractive tool helps to inspire users to realize the advantageous role of linked data in knowledge management.

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Notes

  1. 1.

    Data-Driven Documents (D3) http://d3js.org/.

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Author information

Authors and Affiliations

  1. SOKENDAI (The Graduate University for Advanced Studies), Kanagawa, Japan

    Rathachai Chawuthai & Hideaki Takeda

  2. National Institute of Informatics, Tokyo, Japan

    Rathachai Chawuthai & Hideaki Takeda

Authors
  1. Rathachai Chawuthai
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  2. Hideaki Takeda
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Corresponding author

Correspondence to Rathachai Chawuthai .

Editor information

Editors and Affiliations

  1. Southeast University, Nanjing, China

    Guilin Qi

  2. Osaka University, Ibaraki, Japan

    Kouji Kozaki

  3. The University of Aberdeen, Aberdeen, United Kingdom

    Jeff Z. Pan

  4. Zhongnan Hospital of Wuhan University, Wuhan, China

    Siwei Yu

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Chawuthai, R., Takeda, H. (2016). RDF Graph Visualization by Interpreting Linked Data as Knowledge. In: Qi, G., Kozaki, K., Pan, J., Yu, S. (eds) Semantic Technology. JIST 2015. Lecture Notes in Computer Science(), vol 9544. Springer, Cham. https://doi.org/10.1007/978-3-319-31676-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-31676-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31675-8

  • Online ISBN: 978-3-319-31676-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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