Meta-path guided graph attention network for explainable herb recommendation.

Traditional Chinese Medicine (TCM) has been widely adopted in clinical practice by Eastern Asia people for thousands of years. Nowadays, TCM still plays a critical role in Chinese society and receives increasing attention worldwide. The existing herb recommenders learn the complex relations between symptoms and herbs by mining the TCM prescriptions. Given a set of symptoms, they will provide a set of herbs and explanations from the TCM theory. However, the foundation of TCM is Yinyangism (i.e. the combination of Five Phases theory with Yin-yang theory), which is very different from modern medicine philosophy. Only recommending herbs from the TCM theory aspect largely prevents TCM from modern medical treatment. As TCM and modern medicine share a common view at the molecular level, it is necessary to integrate the ancient practice of TCM and standards of modern medicine. In this paper, we explore the underlying action mechanisms of herbs from both TCM and modern medicine, and propose a Meta-path guided Graph Attention Network (MGAT) to provide the explainable herb recommendations. Technically, to translate TCM from an experience-based medicine to an evidence-based medicine system, we incorporate the pharmacology knowledge of modern Chinese medicine with the TCM knowledge. We design a meta-path guided information propagation scheme based on the extended knowledge graph, which combines information propagation and decision process. This scheme adopts meta-paths (predefined relation sequences) to guide neighbor selection in the propagation process. Furthermore, the attention mechanism is utilized in aggregation to help distinguish the salience of different paths connecting a symptom with a herb. In this way, our model can distill the long-range semantics along meta-paths and generate fine-grained explanations. We conduct extensive experiments on a public TCM dataset, demonstrating comparable performance to the state-of-the-art herb recommendation models and the strong explainability.