LinkLion: A Link Repository for the Web of Data

  • Markus Nentwig
  • Tommaso Soru
  • Axel-Cyrille Ngonga Ngomo
  • Erhard Rahm
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8798)


Links between knowledge bases build the backbone of the Web of Data. Consequently, numerous applications have been developed to compute, evaluate and infer links. Still, the results of many of these applications remain inaccessible to the tools and frameworks that rely upon it. We address this problem by presenting LinkLion, a repository for links between knowledge bases. Our repository is designed as an open-access and open-source portal for the management and distribution of link discovery results. Users are empowered to upload links and specify how these were created. Moreover, users and applications can select and download sets of links via dumps or SPARQL queries. Currently, our portal contains 12.6 million links of 10 different types distributed across 3184 mappings that link 449 datasets. In this demo, we will present the repository as well as different means to access and extend the data it contains. The repository can be found at


Ensemble Learning SPARQL Query Ontology Match Triple Store Question Answering System 
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.


  1. 1.
    Hartung, M., Groß, A., Rahm, E.: Composition methods for link discovery. In: BTW (2013)Google Scholar
  2. 2.
    Kirsten, T., Gross, A., Hartung, M., Rahm, E.: GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution. J. Biomed. Semant. 2, 6 (2011)CrossRefGoogle Scholar
  3. 3.
    Ngonga Ngomo, A.-C.: On link discovery using a hybrid approach. J. Data Semant. 1(4), 203–217 (2012)CrossRefGoogle Scholar
  4. 4.
    Ngonga Ngomo, A.-C., Auer, S.: LIMES: a time-efficient approach for large-scale link discovery on the web of data. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, IJCAI’11, vol. 3, pp. 2312–2317. AAAI Press (2011)Google Scholar
  5. 5.
    Ngonga Ngomo, A.-C., Sherif, M.A., Lyko, K.: Unsupervised link discovery through knowledge base repair. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 380–394. Springer, Heidelberg (2014)Google Scholar
  6. 6.
    Volz, J., Bizer, C., Gaedke, M., Kobilarov, G.: Silk - a link discovery framework for the web of data. In: Bizer, C., Heath, T., Berners-Lee, T., Idehen, K. (eds.) LDOW. CEUR Workshop Proceedings, vol. 538. (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Markus Nentwig
    • 1
  • Tommaso Soru
    • 2
  • Axel-Cyrille Ngonga Ngomo
    • 2
  • Erhard Rahm
    • 1
  1. 1.Database Group, Department of Computer ScienceUniversity of LeipzigLeipzigGermany
  2. 2.AKSW, Department of Computer ScienceUniversity of LeipzigLeipzigGermany

Personalised recommendations