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. 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. Resulting of LOD. Second, domain knowledge can be added to this input and be considered by ILP. We have implemented and applied this approach to the characterisation 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.