O4-N Multilingual Knowledge Alignment with Embedding Representation Learning

PI: Ji Wu

In this project, we will develop innovative medical embedding learning algorithms   and make knowledge alignment across multiple languages in the medical domain. Our learning algorithms will fully exploit the semantic similarity for knowledge alignment across languages. Our model will first encode multilingual knowledge into latent embedding semantic spaces, and then make the knowledge aligned by cross-mapping among these embedding spaces.  We employ data visualization  techniques to help analyzing and designing the embedding learning and space mapping process. We aim at better accuracy than traditional translation based techniques by exploiting semantic information hidden in the knowledge. The application of our aforementioned algorithm enables sharing of medical knowledge across multi-languages and improving the performance of deep learning based diagnosis. We also test the resulted semantic deep learning system for EHR data analysis and diagnosis.