Exploring Term Networks for Semantic Search over RDF Knowledge Graphs

  • Edgard MarxEmail author
  • Konrad Höffner
  • Saeedeh Shekarpour
  • Axel-Cyrille Ngonga Ngomo
  • Jens Lehmann
  • Sören Auer
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 672)


Information retrieval approaches are considered as a key technology to empower lay users to access the Web of Data. A large number of related approaches such as Question Answering and Semantic Search have been developed to address this problem. While Question Answering promises more accurate results by returning a specific answer, Semantic Search engines are designed to retrieve the best top-\(K\) ranked resources. In this work, we propose *path, a Semantic Search approach that explores term networks for querying RDF knowledge graphs. The adequacy of the approach is evaluated employing benchmark datasets against state-of-the-art Question Answering as well as Semantic Search systems. The results show that *path achieves better F\(_1\)-score than the currently best performing Semantic Search system.



This work was supported by a grant from the EU H2020 Framework Programme provided for the projects Big Data Europe (GA no. 644564), HOBBIT (GA no. 688227), and CNPq under the program Ciências Sem Fronteiras.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Edgard Marx
    • 1
    • 3
    Email author
  • Konrad Höffner
    • 1
  • Saeedeh Shekarpour
    • 4
  • Axel-Cyrille Ngonga Ngomo
    • 1
  • Jens Lehmann
    • 2
  • Sören Auer
    • 2
  1. 1.AKSWUniversity of LeipzigLeipzigGermany
  2. 2.Computer Science InstituteUniversity of BonnBonnGermany
  3. 3.Instituto de Pesquisa e Desenvolvimento Albert SchirmerTeófilo OtoniBrazil
  4. 4.Knoesis Research CenterFairbornUSA

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