# A Knowledge Graph Embedding Model Based on Cyclic Consistency--Cyclic CKGE
## 6 Conclusions  
### Intelligent Medical System Function Test  
Due to the rapid development of the Internet and era, the development of a knowledge graph is very rapid, and it has a very good application in many fields, such as sky eye search, and intelligent question and answer of enterprise information. In this paper, intelligent medical treatment is realized by constructing a medical knowledge map, and satisfactory results are obtained. However, due to the limited technology and knowledge mastered at present, and there is no relevant professional doctor to give professional guidance to the constructed medical knowledge map, there are still some functions in the system that need to be improved and upgraded. It is hoped that the system developed in this paper can be put into use in the future to improve the current medical environment. The following is a summary of the technical problems and solutions in the construction of a medical knowledge map and intelligent medical system in this paper:  
(1) Entity nesting problem  
The entity nesting problem is a situation in which a shorter entity is completely contained within another longer entity in a sentence. This kind of problem is very common in natural language processing. In addition to the TPLinker model used in this paper to perfectly solve such problems, in 2021, [PERSON] et al. proposed an effective cascade double decoder method to extract overlapping relational triples [44], which includes a text-specific relational decoder and a relational corresponding entity decoder. A text-specific relational decoder detects relationships in sentences according to their text semantics and uses them as additional features to guide entity extraction. For each extracted relationship with trainable properties, a span labeling scheme is used to detect the corresponding head and tail entities in the relationship. The problem of word overlap is solved.  
(2) Accurately define entity relationship  
Due to the large and complex medical data and the limited labeling data, it is necessary for domain experts to manually define entity relationships in the medical knowledge graph, but this is a very expensive project. In this paper, based on the RotateE model, the entities in the medical atlas are predicted when some relationships in the medical atlas are known, so as to build a relatively complete medical knowledge atlas. However, due to the lack of guidance from medical experts, there are still some uncertain relationships in the atlas. How to accurately define the entity relationship in the graph is also the focus of subsequent research.  
(3) The problem of making medical data sets  
At present, the construction of a medical knowledge map in China is still in its infancy, and there is no professional medical data set in China. It is very difficult to construct a complete and accurate medical knowledge map. In the future, it is hoped that different medical institutions in China can conduct academic research and discussion in this respect,  
Figure 13: Infer complications of related diseases.  
and provide accurate and high-quality medical data for the construction of a medical knowledge graph under the premise of ensuring patient privacy.  
(4) Privacy issues  
Privacy is an important issue for hospitals to consider when releasing information about personal medical records. The purpose of constructing the medical knowledge graph is to solve the medical problems of the public without infringing on the privacy of personal data. At present, medical data are basically distributed separately in different medical institutions, and there is no mining work on the value of medical data among institutions, which has played a certain hindering role in the development and construction of a medical knowledge map. Therefore, it is very important to build a confidential medical database.