Exploring Importance Measures for Summarizing RDF/S KBs

  • Alexandros Pappas
  • Georgia Troullinou
  • Giannis Roussakis
  • Haridimos KondylakisEmail author
  • Dimitris Plexousakis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10249)


Given the explosive growth in the size and the complexity of the Data Web, there is now more than ever, an increasing need to develop methods and tools in order to facilitate the understanding and exploration of RDF/S Knowledge Bases (KBs). To this direction, summarization approaches try to produce an abridged version of the original data source, highlighting the most representative concepts. Central questions to summarization are: how to identify the most important nodes and then how to link them in order to produce a valid sub-schema graph. In this paper, we try to answer the first question by revisiting six well-known measures from graph theory and adapting them for RDF/S KBs. Then, we proceed further to model the problem of linking those nodes as a graph Steiner-Tree problem (GSTP) employing approximations and heuristics to speed up the execution of the respective algorithms. The performed experiments show the added value of our approach since (a) our adaptations outperform current state of the art measures for selecting the most important nodes and (b) the constructed summary has a better quality in terms of the additional nodes introduced to the generated summary.


Semantic summaries Schema summary RDF/S Knowledge Bases Graph theory 



This work was partially supported by the EU projects iManageCancer and CloudSocket under the contracts H2020-643529, H2020-644690.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alexandros Pappas
    • 1
  • Georgia Troullinou
    • 2
  • Giannis Roussakis
    • 2
  • Haridimos Kondylakis
    • 2
    Email author
  • Dimitris Plexousakis
    • 2
  1. 1.Computer Science DepartmentUniversity of CreteHeraklionGreece
  2. 2.Institute for Computer ScienceFORTHHeraklionGreece

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