Ranking the Linked Data: The Case of DBpedia

  • Roberto Mirizzi
  • Azzurra Ragone
  • Tommaso Di Noia
  • Eugenio Di Sciascio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6189)


The recent proliferation of crowd computing initiatives on the web calls for smarter methodologies and tools to annotate, query and explore repositories. There is the need for scalable techniques able to return also approximate results with respect to a given query as a ranked set of promising alternatives. In this paper we concentrate on annotation and retrieval of software components, exploiting semantic tagging relying on Linked Open Data. We focus on DBpedia and propose a new hybrid methodology to rank resources exploiting: (i) the graph-based nature of the underlying RDF structure, (ii) context independent semantic relations in the graph and (iii) external information sources such as classical search engine results and social tagging systems. We compare our approach with other RDF similarity measures, proving the validity of our algorithm with an extensive evaluation involving real users.


Link Data Ranking Algorithm SPARQL Query Seed Node Representative Node 
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

  • Roberto Mirizzi
    • 1
  • Azzurra Ragone
    • 1
    • 2
  • Tommaso Di Noia
    • 1
  • Eugenio Di Sciascio
    • 1
  1. 1.Politecnico di BariBariItaly
  2. 2.University of TrentoPovo (Trento)Italy

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