Reliable Knowledge Graph Reasoning with Uncertainty Quantification

Recently, Knowledge Graphs (KGs) have been successfully coupled with Large Language Models (LLMs) to mitigate their hallucinations and enhance their reasoning capability, e.g., KG-based retrieval-augmented framework for question-answering. However, current KG-LLM frameworks lack rigorous uncertainty estimation, limiting their reliable deployment in high-stake applications where the cost of errors is significant. To address this crucial gap, we propose a new trustworthy KG-LLM framework, UaG(<u>U</u>ncertainty <u>A</u>ware <u>G</u>raph Reasoning), which incorporates uncertainty quantification into the KG-LLM framework. We design an uncertainty-aware multi-step reasoning framework that leverages conformal prediction to provide a theoretical guarantee on the prediction set. To manage the error rate of the multi-step process, we additionally introduce an error rate control module to adjust the error rate within the individual components. Our preliminary results demonstrate that UaG can achieve the desired theoretical coverage while maintaining a reasonable prediction set size.