LECITE: LoRA-Enhanced and Consistency-Guided Iterative Knowledge Graph Construction

Abstract

Knowledge graphs (KGs) offer a structured and collaborative approach to integrating diverse knowledge from various domains. However, constructing knowledge graphs typically requires significant manual effort and heavily relies on pretrained models, limiting their adaptability to specific sub-domains. This paper proposes an innovative, efficient, and locally deployable knowledge graph construction framework that leverages low-rank adaptation (LoRA) to fine-tune large language models (LLMs) in order to reduce noise. By integrating iterative optimization, consistency-guided filtering, and prompt-based extraction, the proposed method achieves a balance between precision and coverage, enabling the robust extraction of standardized subject–predicate–object triples from raw long texts. This makes it highly effective for knowledge graph construction and downstream reasoning tasks. We applied the parameter-efficient open-source model Qwen3-14B, and experimental results on the SciERC dataset show that, under strict matching (i.e., ensuring the exact matching of all components), our method achieved an F1 score of 0.358, outperforming the baseline model’s F1 score of 0.349. Under fuzzy matching (allowing some parts of the triples to be unmatched), the F1 score reached 0.447, outperforming the baseline model’s F1 score of 0.392, demonstrating the effectiveness of our approach. Ablation studies validate the robustness and generalization potential of our method, highlighting the contribution of each component to the overall performance.