Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs
This work addresses the incompleteness of knowledge graphs for applications requiring novel relation prediction with limited data, representing an incremental improvement in few-shot learning techniques.
The paper tackles the problem of few-shot relation learning in knowledge graphs by proposing RelAdapter, a context-aware adapter that enhances meta-learning adaptation, achieving superior performance over state-of-the-art methods on three benchmark datasets.
Knowledge graphs (KGs) are instrumental in various real-world applications, yet they often suffer from incompleteness due to missing relations. To predict instances for novel relations with limited training examples, few-shot relation learning approaches have emerged, utilizing techniques such as meta-learning. However, the assumption is that novel relations in meta-testing and base relations in meta-training are independently and identically distributed, which may not hold in practice. To address the limitation, we propose RelAdapter, a context-aware adapter for few-shot relation learning in KGs designed to enhance the adaptation process in meta-learning. First, RelAdapter is equipped with a lightweight adapter module that facilitates relation-specific, tunable adaptation of meta-knowledge in a parameter-efficient manner. Second, RelAdapter is enriched with contextual information about the target relation, enabling enhanced adaptation to each distinct relation. Extensive experiments on three benchmark KGs validate the superiority of RelAdapter over state-of-the-art methods.