Being able to extract and exploit information that is included in multiple resources (repositories, corpora, etc.) is essential to benefiting from the increasing availability and complementary nature of such data scattered across the World Wide Web. However, such an endeavour raises a number of challenges including dealing with the diverse structures of such resources, different relationships among such data, and the overlapping and complementary nature of the information. Thus, developing a semantic method that can extract semantic information and hidden associations would help overcome such difficulties that occur when dealing with multiple resources. This paper presents a new semantic method that exploits the overlap between various resources with different structures (i.e. ontologies as forms of structured data and corpora as examples of unstructured data) and employs semantic relations, specifically sibling relations, to infer new information that may not exist in the original resources. Then, this method employs the new information in a content-based recommender system to enhance the quality of the provided recommendations (i.e. articles) in complex fields that are inherently characterised by varying relations and structures, such as bioinformatics. In addition, this method is accompanied by an automatic tool that is responsible for tailoring individual recommendations to each user based on his/her profile.