Research on Integrating Explicit/Implicit Semantic Representation and Multimodal Knowledge Graph for Traditional Chinese Medicine Digital Therapy

Longqing Zhang, Lei Yang, Xinwei Zhang, Yungui Chen, Yongjian Huang, Jiawei Zhan

2023

Abstract

The application of Artificial Intelligence (AI) technology is well-suited for Traditional Chinese Medicine (TCM) due to its reliance on observation through "looking, smelling, questioning, and cutting", as well as empirical diagnosis utilizing images, sounds, pulse sensing data, and other factors. This makes TCM an important area for breakthroughs in AI technology. The primary goal of this project is to extract a large quantity of TCM diagnostic knowledge that can be read by computers, train the TCM knowledge map model to become a discriminative model, and allow the model to differentiate between pairs of entities with different relationships or identify meaningful pairs of entities selected from randomly sampled negative entities. Constructing the TCM knowledge graph involves three main modules: TCM knowledge extraction, TCM knowledge fusion, and TCM knowledge computation. TCM knowledge extraction involves identifying the constituent elements of the knowledge graph, such as entities, relationships, and attributes, from vast amounts of semi-structured, structured, or unstructured pharmaceutical data, and determining the most effective method for depositing these elements into the knowledge base. TCM Knowledge Fusion integrates, disambiguates, and processes the contents of the TCM knowledge base, enhancing the logic and expressiveness within the knowledge base, and updating outdated knowledge or supplementing new knowledge for the TCM knowledge graph.

Paper Citation

in Harvard Style

Zhang L., Yang L., Zhang X., Chen Y., Huang Y. and Zhan J. (2023). Research on Integrating Explicit/Implicit Semantic Representation and Multimodal Knowledge Graph for Traditional Chinese Medicine Digital Therapy. In Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT; ISBN 978-989-758-677-4, SciTePress, pages 214-219. DOI: 10.5220/0012277900003807

in Bibtex Style

@conference{anit23,
author={Longqing Zhang and Lei Yang and Xinwei Zhang and Yungui Chen and Yongjian Huang and Jiawei Zhan},
title={Research on Integrating Explicit/Implicit Semantic Representation and Multimodal Knowledge Graph for Traditional Chinese Medicine Digital Therapy},
booktitle={Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT},
year={2023},
pages={214-219},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012277900003807},
isbn={978-989-758-677-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT
TI - Research on Integrating Explicit/Implicit Semantic Representation and Multimodal Knowledge Graph for Traditional Chinese Medicine Digital Therapy
SN - 978-989-758-677-4
AU - Zhang L.
AU - Yang L.
AU - Zhang X.
AU - Chen Y.
AU - Huang Y.
AU - Zhan J.
PY - 2023
SP - 214
EP - 219
DO - 10.5220/0012277900003807
PB - SciTePress