Diagnostic “grey zones” is a term used in pathology, the study of diseases, to describe overlapping morphologic, immunophenotypic and genetic features among various disease subtypes that can lead to diagnostic pitfalls and errors in classifying cancer (e.g. lymphomas). Diagnostic pitfalls are risks that pathologists should be aware of and avoid, and diagnostic errors are failures of medical tests to describe accurately the disease progress in an individual patient. Therefore, pathologists have to perform rigorous medical examinations. These examinations can be used to study diagnostic errors. However, the examinations are documented as unstructured free text. From a computational standpoint, it is difficult to automatically identify diagnostic pitfalls and errors in free text. In this research, we aim to develop a framework for structurized free text; a framework for computer-based summarizations of pathology reports; and a hypothesis generation framework on diagnostic errors. To achieve our aims, we utilize ontological modeling, natural language processing, and data mining techniques.