## 1 Introduction  
### Research Motivation  
Knowledge graphs (KGs) [1] are structured semantic networks that represent complex relations between entities in the form of triples. They have demonstrated significant utility in domains such as information retrieval [2], recommendation systems [3], and question answering [4]. However, real-world KGs are often incomplete, which substantially impairs their effectiveness and applicability in downstream tasks [5]. To address this limitation, considerable efforts have been devoted to developing methods for inferring missing information, a task commonly referred to as knowledge graph completion (KGC). KGC aims to predict plausible triples that are absent from the KGs by leveraging existing factual knowledge [1]. For example, given a query \((h,r,? )\) consisting of a head entity \(h\) and relation \(r\), the goal is to predict the most likely tail entity \(t\) that completes the triple. The mainstream methods for the KGC task are broadly categorized into two groups: embedding-based and LLM-based [6]. The former, including TransE [7] and RotatE [8], model structural patterns in KGs through geometric or algebraic operations, while SimKGC [9] performs KGC by computing semantic similarity between textual entity representations. However, these methods lack the capability for logical reasoning over complex relations or multi-hop inference [10]. On the other hand, LLM-based methods, including models such as GPT-4 [11] and Qwen2.5 [12], are emerging as promising solutions for KGC tasks. Although LLM-based KGC methods exhibit notable advantages in natural language understanding and context modeling, they still face serious challenges regarding generalization and stability. When encountering unseen entity types, rare relations, or domain-shifted knowledge graphs, current models lack explicit structural inductive mechanisms, which limits their ability to generalize. Moreover, due to the absence of systematic modeling for graph-structured constraints and knowledge consistency, LLMs are prone to hallucinations, producing outputs that appear logically coherent but are factually incorrect during tail entity prediction or reasoning path generation. Such hallucinations undermine the credibility of LLMs in KGC tasks and pose potential risks to downstream applications. To address these issues, researchers have explored incorporating external structural information from knowledge graphs, re-ranking candidate entities, and designing structure-aware prompt strategies to guide LLMs toward more robust knowledge reasoning, thereby improving both generalization and output reliability. As shown in Figure 1, LLM-based prediction methods can be categorized into two main paradigms. The first paradigm involves large language models (LLMs) combined with KG, while the second incorporates both KG and chain-of-thought (CoT) prompting [13]. In the paradigm of LLMs combined with KG, KICGPT proposed by [PERSON] et al. first utilizes a pre-trained knowledge graph embedding (KGE) model to predict the top-m candidate entities [14]. Then, prompts are constructed and multi-turn interactions with ChatGPT ([[https://openai.com/](https://openai.com/)]([https://openai.com/](https://openai.com/))) are performed, followed by re-ranking of the candidate entities based on ChatGPT's responses. In contrast, the paradigm of LLMs combined with KG and CoT is represented by the KG-LLM framework proposed by [PERSON] et al. [15], which transforms multi-hop relational paths in the KGs into structured natural language prompt templates. This approach combines CoT [13] reasoning with instruction fine-tuning strategies [16; 17], enabling the model to perform multi-hop relation prediction and entity completion. The overall design significantly enhances the model's ability to capture structural dependencies and improves the controllability of its prediction outputs.