Due to the seemingly endless stream of publications and the tremendous amount of medical data collected. the development of advanced information extraction (IE) and information retrieval (IR) tools is becoming important to ensure continued access to meaningful information from an increasing amount of accumulated data. In this dissertation, IR. and IE are explored in three data domains: biomedical publications data, with a focus on figure search through the Yale Image Finder (YIF), an online biomedical figure discovery tool; clinical claims data, with a focus on finding similar patients using innovative similarity metrics: and linked data, with a focus on data representation and search. The first thesis contribution is a set of advanced YIF IR tools. including a new text region segmentation algorithm. the introduction of search by image type, and improved context-sensitive related figure retrieval. The second thesis contribution is the development of a novel Needleman-Wunsch-based alignment method that facilitates the retrieval of complex sequence-bused data by establishing trajectory similarity. Using the method, claims-based sequential patient data are compared, clustered, and used for the prediction of 30-day readmission after heart failure hospitalization. The third thesis contribution is the successful use of semantics to aid in biomedical information retrieval. Semantic search over VIF content is demonstrated using a new ontology that defines semantic relationships between figures and their data additionally, a schema for the generation of nanopublications is introduced as a means to convey basic scientific findings in an informal way.