KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion
This addresses the long-tail entity issue in knowledge graph completion for downstream applications, but it is incremental as it builds on existing triple-based and text-based methods.
The paper tackled the problem of knowledge graph completion by proposing KICGPT, a framework that integrates a large language model with a triple-based retriever using in-context learning, which achieved effectiveness on benchmark datasets with smaller training overhead and no finetuning.
Knowledge Graph Completion (KGC) is crucial for addressing knowledge graph incompleteness and supporting downstream applications. Many models have been proposed for KGC. They can be categorized into two main classes: triple-based and text-based approaches. Triple-based methods struggle with long-tail entities due to limited structural information and imbalanced entity distributions. Text-based methods alleviate this issue but require costly training for language models and specific finetuning for knowledge graphs, which limits their efficiency. To alleviate these limitations, in this paper, we propose KICGPT, a framework that integrates a large language model (LLM) and a triple-based KGC retriever. It alleviates the long-tail problem without incurring additional training overhead. KICGPT uses an in-context learning strategy called Knowledge Prompt, which encodes structural knowledge into demonstrations to guide the LLM. Empirical results on benchmark datasets demonstrate the effectiveness of KICGPT with smaller training overhead and no finetuning.