Biological literature databases continue to grow rapidly with vital information that is important for conducting sound biomedical research. As data and information space continue to grow exponentially, the need for rapidly surveying the published literature, synthesizing, and discovering the embedded "knowledge" is becoming critical to allow the researchers to conduct "informed" work, avoid repetition, and generate new hypotheses. Knowledge, in this case, is defined as one-to-many and many-to-many relationships among biological entities such as gene, protein, drug, disease, etc. This new knowledge may significantly enhance the ability of biological researchers with diverse objectives to efficiently utilize on-line resources, generate methods for analysis of biological data such as identifying biological pathways, and provide computerized support for disease target and new drug discovery. This research is cross disciplinary as it involves computational techniques applied to structured and unstructured data to extract and synthesize biomedical knowledge.