## 7 Conclusions  
In this work, we have presented GLR, a unified framework for KGC with LLMs, which integrates Graph-CoT-based prompting, parameter-efficient fine-tuning via LoRA, and a confidence assessment mechanism based on P(True) scoring. The proposed framework enables LLMs to perform structured chain-of-thought reasoning under graph structural constraints, thereby enhancing their ability to model knowledge graphs and improving the reliability of reasoning outputs. Extensive experiments conducted on three standard datasets--namely FB15K-237, WN18 RR, and UMLS--show that GLR substantially advances the performance boundaries of LLMs in KGC tasks, particularly in terms of structural awareness, multi-hop reasoning, and confidence-based output modeling. Specifically, the Graph-CoT prompting strategy strengthens the model's capacity to capture multi-hop structural patterns; the LoRA-based fine-tuning mechanism significantly reduces the cost of parameter updates; and the P(True) confidence evaluation method improves both the controllability and credibility of the predictions. Beyond performance improvements, GLR also contributes a novel framework that unifies structural reasoning, parameter-efficient tuning, and confidence estimation, thereby shifting the paradigm from naive generative querying to structure-guided knowledge selection in LLM-based KGC. This paradigm shift provides a scalable and generalizable foundation for future research on knowledge reasoning with LLMs. On the aforementioned datasets, GLR achieves MRR scores of 0.679 on WN18 RR, 0.507 on FB15K-237, and 0.804 on UMLS, significantly outperforming state-of-the-art baselines. Ablation studies further validate the contribution of each component. For example, removing the Graph-CoT module results in a 10.4% drop in MRR, highlighting its critical role in structural reasoning. Moreover, when comparing GLR-augmented models with their base LLM counterparts, GLR consistently outperforms them across all evaluation metrics, achieving MRR improvements of 28.3% on WN18 RR, 24.5% on FB15K-237, and 37.9% on UMLS over the strongest competing baselines. Cross-comparative experiments under different model sizes and training configurations also demonstrate that GLR exhibits favorable sample efficiency, making it suitable for practical deployment in real-world scenarios. From an application perspective, GLR is particularly well-suited for domains such as healthcare and finance, where structural consistency and knowledge accuracy are essential. It supports tasks including complex entity recognition, relation discovery, and high-reliability knowledge generation. Nevertheless, in ultra-large-scale knowledge graphs such as Wikidata, where relation distributions are severely imbalanced or graph structures are extremely sparse, GLR may face challenges including insufficient graph context, prompt length constraints, and reduced reasoning efficiency, all of which can impair its generalization performance. In future research, we aim to explore the integration of continuous KG embeddings into the Graph-CoT prompting paradigm, with the objective of unifying symbolic reasoning and vector-based representation learning, as well as investigating how to construct more effective reasoning paradigms. Moreover, extending GLR to support multi-modal KGs and open-domain scenarios presents an exciting direction for future research. Conceptualization, Y.C. ; Data curation, Y.C. and Y.G. ; Formal analysis, X.D. ; Methodology, Y.C. ; Software, Y.C. and Y.G. ; Supervision, X.D. ; Validation, Y.C. ; Visualization, Y.C. ; Writing--original draft, Y.C. ; Writing--review and editing, X.D. and Y.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.