R-HMF: A Relation-enhanced Hierarchical Multimodal Framework for Few-shot Knowledge Graph Completion

Knowledge graph completion (KGC), which aims at inferring the missing fact triples, has shown an essential role in constructing a complete knowledge graph to enhance downstream applications. However, most KGC techniques require a large number of labeled training instances, and the performance drops dramatically when only a few triples are available. The primary challenge lies in the insufficient information that the few-shot annotated triples provided. Recently, several works have utilized multimodal entity contexts to enrich the entity representation, but their performance remains constrained by 1) overlooking the challenges of modality heterogeneity, 2) introducing the redundant multimodal noise of entities that is irrelevant to the corresponding relation, and 3) the difficulty in learning relation representation with only a few labeled cases. To address the above issues, we propose a novel R elation-enhanced H ierarchical M ultimodal F ramework (R-HMF) for the few-shot knowledge graph completion. Specifically, to take the modality heterogeneity into account, we first conduct the modality-specific few-shot relation learning to capture the correlation between entities and relations within each modality. Subsequently, a multimodal fact assessment module is designed to validate the correctness of given triples by considering the entity contexts in a joint multimodal representation space. Notably, to avoid the involvement of redundant multimodal noise of entities, adaptive entity features are extracted to fit for different relations. In addition, to strengthen the relation representation in the few-shot setting, we also take full advantage of the large language models (LLMs) to generate the corresponding relation names and descriptions, which will be utilized to align different modalities as well. Extensive experimental results on two multimodal knowledge graph datasets, MM-FB15K237 and MM-DBpedia, show that our framework achieves better performance than previous state-of-the-art methods by improving 3.23% Hits@10 score under the 1-shot setting and 6.45% Hits@10 score under the 5-shot setting on average.