Exploiting Semantic Web Datasets: A Graph Pattern Based Approach

  • Honghan WuEmail author
  • Boris Villazon-Terrazas
  • Jeff Z. Pan
  • Jose Manuel Gomez-Perez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 480)


In the last years, we have witnessed vast increase of Linked Data datasets not only in the volume, but also in number of various domains and across different sectors. However, due to the nature and techniques used within Linked Data, it is non-trivial work for normal users to quickly understand what is within the datasets, and even for tech-users to efficiently exploit the datasets. In this paper, we propose a graph pattern based framework for realising a customisable data exploitation. Atomic graph patterns are identified as building blocks to construct facilities in various exploitation scenarios. In particular, we demonstrate how such graph patterns can facilitate quick understandings about RDF datasets as well as how they can be utilised to help data exploitation tasks like concept level browsing, query generation and data enrichment.


Data Exploitation Graph Pattern Query Generation SPARQL Query Basic Graph Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research has been funded by the European Commission within the 7th Framework Programme/Maria Curie Industry-Academia Partnerships and Pathways schema/PEOPLE Work Programme 2011 project K-Drive number 286348 (cf. This work was also supported by NSFC with Grant No. 61105007 and by NUIST with Grant No. 20110429.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Honghan Wu
    • 1
    • 3
    Email author
  • Boris Villazon-Terrazas
    • 2
  • Jeff Z. Pan
    • 1
  • Jose Manuel Gomez-Perez
    • 2
  1. 1.Department of Computing ScienceUniversity of AberdeenAberdeenUK
  2. 2.ISOCO, Intelligent Software Components S.A.MadridSpain
  3. 3.Nanjing University of Information Science and TechnologyNanjingChina

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