DRKG: Faithful and Interpretable Multi-Hop Knowledge Graph Question Answering via LLM-Guided Reasoning Plans  
[PERSON]  
1 School of Computer and Electronic Information, Guangqi University, Nanning 530004, China; [EMAIL_ADDRESS] (Y.C. ); 2213590325@st.  
or relation, multi-hop KGQA requires aligning the natural language query logic with entities and relations in the knowledge graph. This process demands multi-step reasoning, traversing multiple edges in the knowledge graph to derive answers. There are two critical challenges in KGQA: the semantic gap between natural language queries and structured knowledge representations, and the interpretability in reasoning processes [4]. Building upon these challenges, this study specifically addresses the following research questions. RQ1: How can the semantic gap between unstructured natural language queries and structured knowledge graph representations be effectively bridged? RQ2: What mechanisms can enhance the interpretability of multi-hop reasoning processes without compromising KGQA system performance? The semantic gap (RQ1) primarily stems from the fundamental disparity between the structured nature of knowledge graphs and the unstructured characteristics of natural language. Bridging this structural mismatch constitutes a critical prerequisite for effective multi-hop KGQA systems [4]. Traditional approaches address this challenge using embedding-based methods, which map entities and relations into continuous vector spaces. These embeddings are then combined with word vectors to compute answers through vector-space operations. Although such methods partially mitigate the semantic gap, they suffer from inherent limitations: their internal decision-making mechanisms remain opaque to users (RQ2), acting as a \"black box\" that outputs answers without transparently revealing the reasoning process [5]. Ideally, multi-hop KGQA systems should not only provide correct answers but also elucidate their reasoning paths [6]. To address these challenges, the DRKG innovatively integrates the explicit reasoning capabilities of LLMs with the structured retrievability of a knowledge graph. During reasoning, the DRKG first inputs linearized knowledge graph information into LLMs, which dynamically filter and select relevant information. Subsequently, leveraging the structured organization of the knowledge graph, the system efficiently retrieves answers based on the filtered information. Our framework is the explicit reasoning plan, which acts as a logical boundary controller to ensure both interpretability and controllability. Unlike previous methods, the DRKG employs LLMs to generate structured reasoning plans through semantic parsing. These plans explicitly define two critical constraints: hop thresholds to limit traversal depth in the knowledge graph and logical relation constraints. This mechanism enforces strict adherence to predefined reasoning paths during the traversal of the knowledge graph, effectively mitigating the risks of knowledge hallucination and path divergence. Furthermore, the entire reasoning process becomes transparent and  
Figure 1: An example of a three-hop question answering reasoning process. aligns with human-interpretable logic, addressing long-standing interpretability concerns in multi-hop KGQA. We evaluated the DRKG's accuracy on four widely used benchmark datasets and tested its faithfulness on two of these datasets. The experimental results demonstrate that the DRKG's design significantly enhances multi-hop KGQA performance while delivering interpretable reasoning processes that align more closely with human reasoning patterns. The key contributions of this work are summarized as follows:  
1. Synergistic reasoning plan-knowledge retrieval mechanism: We propose a novel framework that synchronously addresses interpretability deficits and path divergence in multi-hop KGQA through reasoning instructions.