Mei Yong Zhang
ABSTRACT
The continuous increase of medical data promotes the development of knowledge graphs in specific fields. Currently, lung cancer ranks first in terms of morbidity and mortality. In the context of medical intelligence, the top-down and bottom-up methods are combined to semi-automatically construct a knowledge graph of lung cancer. The constructed domain knowledge graph is usually incomplete and lacking the relationship between entities. Therefore, the paper presents a real-time reasoning method of graph attention network to automatically learn reasoning from knowledge graph. This method not only considers neighbor information, but also introduces an attention mechanism, and it can make full use of all nodes and edges in the knowledge graph. The convergence time is reduced by changing the activation function and loss function in the network. This paper adopts three traditional data sets and a new data set for experiment comparison. The experiment results show the proposed method has good performance.
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