Study on the Inference of Knowledge Graph of TCM Ancient Books Based on Deep Reinforcement Learning
10.19879/j.cnki.1005-5304.202310597
- VernacularTitle:基于深度强化学习的中医古籍图谱推理研究
- Author:
Yueyue LIU
1
;
Yan LI
;
Chunyu LI
;
Xueli LIU
Author Information
1. 甘肃中医药大学信息工程学院,甘肃 兰州 730000
- Keywords:
knowledge graph;
knowledge reasoning;
TCM ancient books;
data mining
- From:
Chinese Journal of Information on Traditional Chinese Medicine
2024;31(6):54-59
- CountryChina
- Language:Chinese
-
Abstract:
Objective To address the issue of missing entity relationship information in the knowledge graph of TCM ancient books;To propose a knowledge reasoning method based on deep reinforcement learning;To improve the completeness of the knowledge graph.Methods Based on the benchmark dataset in the field of knowledge graph and the dataset of TCM ancient books,a deep reinforcement learning inference method was adopted.Combined with knowledge one-step completion and multi hop path search,Python language was used in conjunction with the Neo4j graph database to achieve path to path inference.Results In the knowledge reasoning task dataset,compared with the baseline model,this reasoning method improved the MAP index by about 53.79%.When applied to the knowledge graph of TCM ancient books,its evaluation index reached the highest of 0.776.Conclusion The knowledge reasoning method based on deep reinforcement learning has significant advantages in improving the completeness of the knowledge graph of TCM ancient books,and this method can effectively fill the information gap in the graph of TCM ancient books.
