Mining Linked Open Data: A Case Study with Genes Responsible for Intellectual Disability

  • Gabin Personeni
  • Simon Daget
  • Céline Bonnet
  • Philippe Jonveaux
  • Marie-Dominique Devignes
  • Malika Smaïl-Tabbone
  • Adrien Coulet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8574)


Linked Open Data (LOD) constitute a unique dataset that is in a standard format, partially integrated, and facilitates connections with domain knowledge represented within semantic web ontologies. Increasing amounts of biomedical data provided as LOD consequently offer novel opportunities for knowledge discovery in biomedicine. However, most data mining methods are neither adapted to LOD format, nor adapted to consider domain knowledge. We propose in this paper an approach for selecting, integrating, and mining LOD with the goal of discovering genes responsible for a disease. The selection step relies on a set of choices made by a domain expert to isolate relevant pieces of LOD. Because these pieces are potentially not linked, an integration step is required to connect unlinked pieces. The resulting graph is subsequently mined using Inductive Logic Programming (ILP) that presents two main advantages. First, the input format compliant with ILP is close to the format of LOD. Second, domain knowledge can be added to this input and considered by ILP. We have implemented and applied this approach to the characterization of genes responsible for intellectual disability. On the basis of this real-world use case, we present an evaluation of our mining approach and discuss its advantages and drawbacks for the mining of biomedical LOD.


Intellectual Disability Intellectual Disability Inductive Logic Programming SPARQL Query Link Open Data 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Gabin Personeni
    • 1
  • Simon Daget
    • 1
  • Céline Bonnet
    • 2
    • 3
  • Philippe Jonveaux
    • 2
    • 3
  • Marie-Dominique Devignes
    • 1
  • Malika Smaïl-Tabbone
    • 1
  • Adrien Coulet
    • 1
  1. 1.LORIA (CNRS, Inria NGE, Université de Lorraine)Vandoeuvre-lès-NancyFrance
  2. 2.Laboratoire de Génétique MédicaleCentre Hospitalier Universitaire de NancyVandoeuvre-lès-NancyFrance
  3. 3.INSERM U-954, Université de LorraineVandoeuvre-lès-NancyFrance

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