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Improving link prediction models through a performance enhancement scheme:a study on semi-supervised learning and model soup

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The Journal of China Universities of Posts and Telecommunications 2024年第4期31卷 43-53页

作者： Qi Donglin Chen Shudong Du Rong Yu Yong Tong Da Intelligent Manufacturing Electronics Research and Development Center Institute of Microelectronics of the Chinese Academy of SciencesBeijing 100029China School of Integrated Circuits University of Chinese Academy of SciencesBeijing 100049China

As an important method for knowledge graph(KG)complementation,link prediction has become a hot research topic in recent *** this paper,a performance enhancement scheme for link prediction models based on the idea of semi-supervised learning and model soup is proposed,which effectively improves the model performance on several mainstream link prediction models with small changes to their *** novel scheme consists of two main parts,one is predicting potential fact triples in the graph with semi-supervised learning strategies,the other is creatively combining semi-supervised learning and model soup to further improve the final model performance without adding significant computational *** validate the effectiveness of the scheme for a variety of link prediction models,especially on the dataset with dense *** terms of CompGCN,the model with the best overall performance among the tested models improves its Hits@1 metric by 14.7%on the FB15K-237 dataset and 7.8%on the WN18RR dataset after using the enhancement ***,it is observed that the semi-supervised learning strategy in the augmentation scheme has a significant improvement for multi-class link prediction models,and the performance improvement brought by the introduction of the model soup is related to the specific tested models,as the performances of some models are improved while others remain largely unaffected.

关键词： natural language processing knowledge graph(KG) link prediction model soup semi-supervised learning

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