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RDF Graph Visualization by Interpreting Linked Data as Knowledge

  • Rathachai Chawuthai
  • Hideaki Takeda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9544)

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.

Keywords

Graph simplification Knowledge representation Linked data RDF visualization Semantic web application Triple ranking 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.SOKENDAI (The Graduate University for Advanced Studies)KanagawaJapan
  2. 2.National Institute of InformaticsTokyoJapan

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