Generative adversarial meta-learning knowledge graph completion for large-scale complex knowledge graphs

Abstract

In the study of large-scale complex knowledge graphs, due to the incompleteness of knowledge and the existence of low-frequency knowledge samples, existing knowledge graph complementation methods are often limited by the amount of data and ignore the complex semantic information. To solve this problem, this paper proposes a knowledge graph completion method CGAML based on the combination of Conditional Generative Adversarial Network and Meta-Learning, which utilizes the hierarchical background knowledge as the basis and introduces conditional variables in the Generative Adversarial Network to represent the required semantic information to constrain the semantic attributes of the generated knowledge. In addition, we design a meta-learning multi-task framework to embed Conditional Generative Adversarial Networks into the meta-learning process and propose local constraints and global gradient optimization strategies to quickly adapt to new tasks and improve computational efficiency. Empirically, our method demonstrates superior performance in realizing few-shot link prediction when compared to existing representative methods.