TransNet: Transfer Knowledge for Few-shot Knowledge Graph Completion
This work addresses incomplete knowledge graphs with long-tail distributions, which degrade downstream applications, by providing a more effective few-shot learning approach, though it appears incremental as it builds on existing transfer and meta-learning techniques.
The paper tackles the problem of few-shot knowledge graph completion for long-tail relations by proposing TransNet, a transfer learning-based method that leverages correlations between tasks and meta-learning to improve performance on novel relations, achieving superior results over state-of-the-art methods on benchmark datasets.
Knowledge graphs (KGs) are ubiquitous and widely used in various applications. However, most real-world knowledge graphs are incomplete, which significantly degrades their performance on downstream tasks. Additionally, the relationships in real-world knowledge graphs often follow a long-tail distribution, meaning that most relations are represented by only a few training triplets. To address these challenges, few-shot learning has been introduced. Few-shot KG completion aims to make accurate predictions for triplets involving novel relations when only a limited number of training triplets are available. Although many methods have been proposed, they typically learn each relation individually, overlooking the correlations between different tasks and the relevant information in previously trained tasks. In this paper, we propose a transfer learning-based few-shot KG completion method (TransNet). By learning the relationships between different tasks, TransNet effectively transfers knowledge from similar tasks to improve the current task's performance. Furthermore, by employing meta-learning, TransNet can generalize effectively to new, unseen relations. Extensive experiments on benchmark datasets demonstrate the superiority of TransNet over state-of-the-art methods. Code can be found at https://github.com/lihuiliullh/TransNet/tree/main