Summary Models for Routing Keywords to Linked Data Sources

  • Thanh Tran
  • Lei Zhang
  • Rudi Studer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6496)


The proliferation of linked data on the Web paves the way to a new generation of applications that exploit heterogeneous data from different sources. However, because this Web of data is large and continuously evolving, it is non-trivial to identify the relevant link data sources and to express some given information needs as structured queries against these sources. In this work, we allow users to express needs in terms of simple keywords. Given the keywords, we define the problem of finding the relevant sources as the one of keyword query routing. As a solution, we present a family of summary models, which compactly represents the Web of linked data and allows to quickly find relevant sources. The proposed models capture information at different levels, representing summaries of varying granularity. They represent different trade-offs between effectiveness and efficiency. We provide a theoretical analysis of these trade-offs and also, verify them in experiments carried out in a real-world setting using more than 150 publicly available datasets.


Relevant Source Keyword Query Summary Model Valid Plan Database Selection 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Thanh Tran
    • 1
  • Lei Zhang
    • 1
  • Rudi Studer
    • 1
  1. 1.Institute AIFBKarlsruhe Institute of TechnologyGermany

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