## 5 Results
### Ablation Study  
To better understand the role of each major component within the GLR framework, we performed ablation experiments on the FB15K-237 dataset. Specifically, we individually remove three core components of GLR, which are Graph-CoT prompting, LoRA-based parameter-efficient fine-tuning, and P(True)-based confidence re-ranking, while retaining the remaining parts of the framework to retrain or infer the model. We quantitatively assessed the contribution of each component by comparing the complete GLR framework (referred to as Full) with its ablated counterparts, thereby evaluating their respective impact on model effectiveness. Table 4 illustrates the results of the full GLR framework and its ablated variants on the FB15K-237 dataset in terms of MRR and Hits@_K_.  
As presented in Table 4, the exclusion of any single component from the GLR framework resulted in a noticeable decline in performance, highlighting the critical role of each module. The Graph-CoT module provides LLMs with a reasoning framework constrained by the structure of the knowledge graph, enhancing the model's ability to handle complex relational chains. Its effectiveness is clearly validated by the ablation study results.  
Through LoRA fine-tuning, LLMs are able to learn representations and reasoning patterns specific to the target knowledge graph, bridging the gap between pre-trained language models and the downstream triple prediction task. Furthermore, the P(True)-based confidence evaluation assigns credibility scores to candidate entities generated by LLMs, effectively filtering out misleading entities that may accidentally receive high prediction scores. This mechanism reduces the risk of incorrect predictions caused by the inherent uncertainty of open-ended generation. In summary, the combination of Graph-CoT prompting, LoRA fine-tuning, and P(True)-based confidence evaluation enables GLR to achieve the best overall performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**FB15K-237**} \\ \cline{2-5}  & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline GLR (Full) & **0.507** & **0.402** & **0.511** & **0.643** \\ w/o Graph-CoT & 0.454 & 0.388 & 0.487 & 0.602 \\ w/o LoRA & 0.374 & 0.245 & 0.401 & 0.493 \\ w/o Confidence Ranking & 0.462 & 0.372 & 0.483 & 0.617 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the ablation study for GLR on the FB15K-237 dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**UMLS**} \\ \cline{2-5}  & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline KG-BERT & 0.648\({}_{\pm 0.021}\) & 0.539\({}_{\pm 0.025}\) & 0.714\({}_{\pm 0.018}\) & 0.839\({}_{\pm 0.015}\) \\ \& CP-KGC & 0.798\({}_{\pm 0.014}\) & 0.676\({}_{\pm 0.016}\) & 0.897\({}_{\pm 0.012}\) & 0.980\({}_{\pm 0.009}\) \\ SimKGC & 0.688\({}_{\pm 0.018}\) & 0.579\({}_{\pm 0.020}\) & 0.748\({}_{\pm 0.017}\) & 0.894\({}_{\pm 0.014}\) \\ \& CP-KGC & 0.780\({}_{\pm 0.013}\) & 0.678\({}_{\pm 0.015}\) & 0.857\({}_{\pm 0.011}\) & 0.951\({}_{\pm 0.009}\) \\ GLR (ours) & 0.804\({}_{\pm 0.012}\) & 0.715\({}_{\pm 0.014}\) & 0.893\({}_{\pm 0.010}\) & 0.962\({}_{\pm 0.008}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: KGC performance of different models on the UMLS. The notation & indicates that the model is optimized using the CP-KGC framework. The best scores for each metric are shown in underline.