An approach to calculation and visualization of efficacies of Traditional Chinese Medical formulae based on a semantic network Traditional Chinese Medicine (TCM) is widely used and tested in ethnic communities for a long history. A large number of TCM formulae, as the centralized expression of xe2x80x9ctheory, cure, recipe, and medicinexe2x80x9d, were preserved and recorded in way of relatively completed ingredients and lack of wellnoted efficacies. The main objective of this paper is to translate dosage of each herb in a TCM formula into intensity of its efficacies in a quantitative and visual way. In this study, firstly, we estimated the intensity between a herb and its efficacies quantitatively based on clinical case data, which can reflect the difference between main and secondary efficacies of a herb. Then, to calculate and visualize the interaction between different efficacies from different herbs, we have proposed a method based on the Efficacy Semantic Network (ESN). It includes three steps: (1) Simple cumulation; (2) Adjustments; (3) Main efficacies selection. We tested on a dataset of 70 TCM formulae, results show that maximum performance measure reaches 0.818 which prove that the proposed approach could help standardize existing and predict potential efficacies of TCM in a quantitative way. It shows a great potential of semantic technologies to represent, calculate and visualize the TCM knowledge. However, more efforts are still required to investigate how to represent other fuzzy and rulebased knowledge and bring more data of TCM formulae for extensive tests.

TCM Translator: A Sequence Generation Approach for Prescribing Herbal Medicines In traditional Chinese medicine (TCM), herbal prescriptions are accumulated from doctorsu0027 clinical experience and play an essential role in treatment process for more than thousands of years. Mining their containing manytomany relationship between symptoms and herbs are important for both clinical practice and novel prescriptions development. Previously, the research attention in this field is attracted by topic models and multilabel classification approaches, but they donu0027t fully capture the correlations of labels and achieve very promising performance. Alternatively, we problematic it as a machine translation problem, that the source sentence is formed by symptoms and the target sentence consists of herbs. Two sentences are both in weakly order. In terms of this assumption, we propose a novel sequencetosequence (seq2seq) architecture, namely TCM translator, to translate symptoms to herbs. Seq2seq, consisting of an encoder and a decoder, is a well known framework for resolving the machine translation problem in natural language processing (NLP) community. It maps a bag of symptoms to the latent space, which is decoded to the target sentence, i.e., herbs. In this paper, transformer is used as our encoder to explore the latent correlations of symptoms and LSTM is implemented as the decoder to generate the herbs sequentially. The experimental results demonstrate that our proposed approach is effective and outperforms other baseline models by a substantial margin.