A Knowledge Graph Embedding Model Based on Cyclic Consistency—Cyclic_CKGE
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Graph Neural Networks · Topic Modeling · Biomedical Text Mining and Ontologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronic and Electrical Engineering, Ningxia University, Yinchuan 750021, China
Key Laboratory of Desert Information Intelligent Perception, Ningxia University, Yinchuan 750021, China
Funding
National Natural Science Foundation of China · Central Government supported Local Special Fund Project of China
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