TCMKG: A Deep Learning Based Traditional Chinese Medicine Knowledge Graph Platform
Ziqiang Zheng, Yongguo Liu, Yun Zhang, Chuanbiao Wen
Abstract
As an effective and novel knowledge management technology, knowledge graph can provide a new way for the inheritance and development of traditional Chinese medicine (TCM). However, the construction of the knowledge graph of TCM is still mainly based on structured data at present. With the accumulation of literatures and electronic medical records, a large amount of knowledge is stored in unstructured texts which urgently needs to be extracted for learning. In this study, we extract TCM core concepts and build ontology layer by analyzing the process of TCM diagnosis and treatment. Then we use deep learning to extract entities and their relations for building TCM knowledge graph from unstructured data. Finally, we build an end-to-end platform TCMKG based on knowledge graph, which can provide functions such as knowledge retrieval, visualization and data management for helping the learning and sharing of TCM knowledge.