Key to achieving more effective machine intelligence is the capability to generalise knowledge across different contexts. In this thesis, we develop a new and very general perspective on knowledge sharing that unifi es and generalises many existing methodologies, while being practically effective, simple to implement, and opening up new problem settings. Knowledge sharing across tasks and domains has conventionally been studied disparately. We fi rst introduce the concept of a semantic descriptor and a flexible neural network approach to knowledge sharing that together unify multi-task/multi-domain learning, and encompass various classic and recent multi-domain learning (MDL) and multi-task learning (MTL) algorithms as special cases. We next generalise this framework from single-output to multi-output problems and from shallow to deep models. To achieve this, we establish the equivalence between classic tensor decomposition methods, and specifi c neural network architectures. This makes it possible to implement our framework within modern deep learning stacks. We present both explicit low-rank, and trace norm regularisation solutions. From a practical perspective, we also explore a new problem setting of zero-shot domain adaptation (ZSDA) where a model can be calibrated solely based on some abstract information of a new domain, e.g., some metadata like the capture device of photos, without collecting or labelling the data.

The increasingly high costs of health care in the U.S. have led the general public to search for different medical approaches. Since the 1990's, the use of Complementary and Alternative Medicine (CAM) has radically increased in the U.S. due to its approach to treat physical, mental, and emotional causes of illness. In 2009, the National Health Statistics reported the impact of CAM in the U.S. health care economy, with population expenditures of $14.8 billion out-of-pocket on natural Medicine and $12.4 billion out-of-pocket on visits to CAM providers as a complement to Western Medicine care. CAM interconnects human functions to reach a balanced state, whereas Western Medicine focuses on specialties and body systems. Both Western Medicine and CAM are unlimited sources of knowledge that follow different approaches but that have the common goal of improving patients' well-being. Identifying relationships between Alternative and Western Medicine can open a completely new approach for health care that can increase understanding of human medical conditions, and facilitate the development of new and more cost-effective treatments. However, the abundance and dissimilarity of CAM and Western Medicine data makes knowledge correlation and management an extremely challenging task. The objective of this research is to design the framework for a knowledge management system to organize, store, and manage the abundant data available for Western Medicine and CAM, and to establish key relationships between the two practices for an effective exploration of ideas and possible solutions for medical diagnosis. Three main challenges in the design of the proposed framework are addressed: data acquisition and modeling; data organization, storage and transfer; and information distribution for further generation and sharing of medical knowledge. A framework to relate the diagnosis process in Western Medicine and Traditional Chinese Medicine, as one of the various forms of CAM, is presented based on process-oriented analysis, hierarchical knowledge representation, relational database, and interactive interface for system utilization. The research is demonstrated using a case study on chronic prostatitis, and can be scalable to other medical conditions. The presented system for knowledge management is not intended to provide a definite solution for medical diagnosis, but to enable the exploration and discovery of knowledge for relational medical diagnosis. The results of this research will positively impact information distribution and knowledge generation via interactive medical knowledge systems, development of new skills for diagnosis and treatment, and a broader understanding of medical diseases and treatments.