The Journal of China Universities of Posts and Telecommunications ›› 2024, Vol. 31 ›› Issue (4): 43-53.doi: 10.19682/j.cnki.1005-8885.2024.1015

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Improving Link Prediction Models through a Performance Enhancement Scheme: A Study on Semi-Supervised Learning and Model Soup

  

  • Received:2023-06-26 Revised:2024-03-27 Online:2024-08-31 Published:2024-08-31
  • Contact: shudong chen E-mail:chenshudong@ime.ac.cn

Abstract: As a fact, most constructed knowledge graphs are far from complete no matter its size. This incompleteness will cause negative influence on the applications based on knowledge graphs. As an important method for knowledge graph complementation, link prediction has become a hot research topic in recent years. In 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 architecture. This novel scheme consists of two main parts: (1) predicting potential fact triples in the graph with semi-supervised learning strategies, (2) creativily combining semi-supervised learning and model soup to further improve the final model performance without adding significant computational overhead. We experimentally validate the effectiveness of the scheme for a variety of link prediction models, especially on the dataset with dense relationships. In 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 scheme. Meanwhile, we observe that the semi-supervised learning strategy in the augmentation scheme has 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, because performance of some models are improved while others remained largely unaffected.

Key words: natural language processing, knowledge graph, link prediction, model soup, semi-supervised learning