MLKGC: Large Language Models for Knowledge Graph Completion Under Multimodal Augmentation
Pengfei Yue, Hailiang Tang, Wanyu Li, Wenxiao Zhang, Bingjie Yan
Source abstract
Knowledge graph completion (KGC) is a critical task for addressing the incompleteness of knowledge graphs and supporting downstream applications. However, it faces significant challenges, including insufficient structured information and uneven entity distribution. Although existing methods have alleviated these issues to some extent, they often rely heavily on extensive training and fine-tuning, which results in low efficiency. To tackle these challenges, we introduce our MLKGC framework, a novel approach that combines large language models (LLMs) with multi-modal modules (MMs). LLMs leverage their advanced language understanding and reasoning abilities to enrich the contextual information for KGC, while MMs integrate multi-modal data, such as audio and images, to bridge knowledge gaps. This integration augments the capability of the model to address long-tail entities, enhances its reasoning processes, and facilitates more robust information integration through the incorporation of diverse inputs. By harnessing the synergy between LLMs and MMs, our approach reduces dependence on traditional text-based training and fine-tuning, providing a more efficient and accurate solution for KGC tasks. It also offers greater flexibility in addressing complex relationships and diverse entities. Extensive experiments on multiple benchmark KGC datasets demonstrate that MLKGC effectively leverages the strengths of both LLMs and multi-modal data, achieving superior performance in link-prediction tasks.
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