Understanding complex procurement information landscapes and exploring how procurement information can be used to support strategic decision-making is important with the increasing amount of information available in the WWW. In this paper, the authors cope with this challenge and describe how data mining techniques can be applied on semantically linked data to estimate the number of bidders in public contracts. They introduce a general approach in order to convert linked data in a relational format which can be used by traditional machine learning approaches.