A manifold learning system that IBM Watson team developed for medical relation extraction: There exists a vast amount of knowledge sources and ontologies in the medical domain. Such information is also growing and changing extremely quickly, making the information difficult for people to read, process and remember. The combination of recent developments in information extraction and the availability of unparalleled medical resources thus offers us the unique opportunity to develop new techniques to help healthcare professionals overcome the cognitive challenges they face in differential diagnosis. In this talk, I will present a manifold learning system that IBM Watson team developed for medical relation extraction. Our model is built upon a medical corpus containing 11 gigabyte text and designed to accurately and efficiently detect the key medical relations that can facilitate clinical decision making. To address the big data challenges, our approach integrates domain specific parsing and typing systems, and can utilize labeled as well as unlabeled examples.