# A Knowledge Graph Embedding Model Based on Cyclic Consistency--Cyclic CKGE
###### Abstract  
Most of the existing medical knowledge maps are incomplete and need to be completed/predicted to obtain a complete knowledge map. To solve this problem, we propose a knowledge graph embedding model (Cyclic_CKGE) based on cyclic consistency. The model first uses the \"graph\" constructed with the head entity and relationship to predict the tail entity, and then uses the \"inverse graph\" constructed with the tail entity and relationship to predict the head entity. Finally, the semantic space distance between the head entity and the original head entity should be very close, which solves the reversibility problem of the network. The Cyclic_CKGE model with a parameter of 0.46 M has the best results on FB15k-237, reaching 0.425 Hits@10. Compared with the best model R-GCN, its parameter exceeds 8 M and reaches 0.417 Hits@10. Overall, Cyclic_CKGE's parametric efficiency is more than 17 times that of R-GCNs and more than 8 times that of DistMult. In order to better show the practical application of the model, we construct a visual medical information platform based on a medical knowledge map. The platform has three kinds of disease information retrieval methods: conditional query, path query and multi-symptom disease inference. This provides a theoretical method and a practical example for realizing knowledge graph visualization. knowledge graph; intelligent medical systems; triple extraction; disease relational reasoning 202313280. 12380. 12380. Knowledge Graph [8; 9]. Subsequently, a knowledge graph began to rapidly extend from search engines to finance [10], agriculture [11], medical [12; 13] and other industries, and became an indispensable technical means for integrating and analyzing data in the era of big data. In recent years, due to the rapid development of information technology and the popularization of a medical information management system, massive medical knowledge and clinical diagnosis data have emerged. Reasonable use of these medical data to build a complete medical database is a prerequisite for promoting intelligent medical care. And a complete professional medical database for the promotion of medical knowledge retrieval, medical-assisted diagnoses and intelligent management of medical records also has a very great role in promoting. The use of a knowledge graph to mine and manage complex medical data, and simply represent them as nodes and relationships, has become a hot topic in academic research. In 2022, [PERSON] [14] adopted the nonlinear transformation method to solve the semantic resolution problem in representation learning. By selectively weighing the semantic elements of entity vectors, the representation and differentiation of different relational semantics can be realized. The proposed algorithm has good semantic distinction ability for complex relation types and related entities, and can effectively improve the relational inference accuracy on a knowledge graph. In order to solve the problem of asymmetry of relationships and make the model adapt to different types of network relationship reasoning tasks, in 2020, Baidu, Inc. [15], based on the existing entity relationship data in the knowledge graph, adopted recurrent neural networks to conduct semantic coding of symbol combinations, and then proposed a neural network model based on symbolic semantic mapping. The model is applied to the distributed representation of a learning graph, which makes the model perform well in a knowledge graph expansion task and graph-based multi-label classification task. To improve the overall recall rate of the system and avoid cascading errors, [PERSON] [16] proposed a Simultaneous Neural Entity-Relation Linker (SNERL) model in 2021. Input text is encoded through an attention mechanism to obtain the context of each entity mentioned in the input. These contextualized representations are then used to predict the distribution of references to entities on the hierarchy and the distribution of references to relationships on the hierarchy.