Multi-LLM collaborative hyper-relational knowledge graph construction with automatic prompt optimization

Hyper-relational knowledge graph (HRKG), equipped with structures such as superedges, can represent n-ary relations and additional attributes, allowing for a more comprehensive depiction of complex facts and scenarios. Existing methods for constructing KGs are typically optimized for specific dataset, resulting in models with insufficient generalizability. To address this gap, we take full advantage of the multi-task generalization capabilities of large language model (LLM) and propose HKG-MLLM, a framework utilizes multiple lightweight open-source LLMs to collaborate and extract knowledge from text based on highly directive prompts, thereby constructing HRKGs. The framework consists of three key modules: (1) Prompt optimization, which generates optimized prompts based on pre-labeled data. (2) HRKG extraction, which extracts information using the optimized prompts from the previous step. (3) HRKG correction, which ensures the accuracy and consistency of the constructed graph. Our experiments on HyperRED demonstrate that HKG-MLLM achieves significant improvements in precision, recall, and F1 score compared to existing methods based on LLM. This framework effectively addresses the generalizability issue and provides a robust solution for knowledge extraction and representation, paving a way for more versatile and accurate HRKGs.