Enriching Medcial Terminology Knowledge Bases via Pre-trained Language Model and Graph Convolutional Network
TABLE II: Experimental Results of State-of-the-art Methods and Our Proposed Model
['Methods', 'Hits@1', 'Hits@5', 'Hits@10']
From this table, we clearly observe that our model achieves much higher scores among all these reference algorithms with the Hits@1 of 59.58%, the Hits@5 of 84.01% and the Hits@10 of 87.63%, which means the combination of the pre-trained model and GCN in our model is complementary. The introduction of pre-trained representations can make full use of both the unsupervised pre-training and supervised training data for better enriching. GCN can effectively utilize the structural characteristics of KB and integrate the neighbor information of the entity into its own representation. Meanwhile, among all baselines, Zhang et al. [zhang2018effective] is the strongest one while Wang et al. [wang2018using] is the worst. The reason for the phenomenon is that a small amount of training data is enough for literal similarity calculated by Zhang et al. [zhang2018effective], and the performance of Wang et al. [wang2018using] depends largely on their knowledge graph, which does not cover enough required knowledge in the experiments.