We are living within the age of information. The ever increasing flow of data and publications poses a monumental bottleneck to scientific progress as despite the amazing abilities of the human mind, it is woefully inadequate in processing such a vast quantity of multidimensional information. The small bits of flotsam and jetsam that we leverage belies the amount of useful information beneath the surface. It is imperative that automated tools exist to better search, retrieve, and summarize this content. Combinations of document indexing and search engines can quickly find you a document whose content best matches your query - if the information is all contained within a single document. But it doesn’t draw connections, make hypotheses, or find knowledge hidden across multiple documents. Literature-based discovery is an approach that can uncover hidden interrelationships between topics by extracting information from existing published scientific literature. The proposed study utilizes a semantic-based approach that builds a graph of related concepts between two user specified sets of topics using semantic predications. In addition, the study includes properties of bibliographically related documents and statistical properties of concepts to further enhance the quality of the proposed intermediate terms. Our results show an improvement in precision-recall when incorporating citations.