Pre-Conference Talk by LIU Ran | Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs

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. 

This is a Pre-Conference talk for The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024).

LIU Ran is a PhD Candidate in Computer Science at the SMU School of Computing and Information Systems, supervised by Assistant Prof. FANG Yuan.  His research is focused on Artificial Intelligence & Data Science and the main research interest lies in the topic of knowledge graph representation learning.