SKRAG : A Retrieval-Augmented Generation Framework Guided by Reasoning Skeletons over Knowledge Graphs (2025)

Abstract: In specialized domains such as space science and utilization, question answering (QA) systems are required to perform complex multi-fact reasoning over sparse knowledge graphs (KGs). Existing KG-based retrieval-augmented generation (RAG) frameworks often face challenges such as inefficient subgraph retrieval, limited reasoning capabilities, and high computational costs. These issues limit their effectiveness in specialized domains. In this paper, we propose SKRAG, a novel Skeleton-guided RAG framework for knowledge graph question answering (KGQA). SKRAG leverages a lightweight language model enhanced with the Finite State Machine (FSM) constraint to produce structurally grounded reasoning skeletons, which guide accurate subgraph retrieval. The retrieved subgraph is then used to prompt a general large language model (LLM) for answer generation. We also introduce SSUQA, a KGQA dataset in the space science and utilization domain. Experiments show that SKRAG outperforms strong baselines on SSUQA and two general-domain benchmarks, demonstrating its adaptability and practical effectiveness.

## introduction in some specialized domains such as space science and utilization, relevant questions often involve interdisciplinary knowledge, multi-fact reasoning, and complex relational structures. as a result, traditional keyword-based retrieval methods struggle to meet the demand for accurate and efficient knowledge acquisition, highlighting the need for intelligent question answering (qa) systems. recently, large language models (llms) have shown impressive capabilities in language understanding and generation tasks @xcite @xcite @xcite , offering new potential for qa. however, without external knowledge grounding, * corresponding author. llms are prone to hallucinations and outdated information @xcite , limiting their reliability in critical domains. to address these issues, retrieval-augmented generation (rag) framework has emerged as a promising solution. by integrating external knowledge sources into the generation process, rag enhances the factual accuracy and interpretability of llms outputs @xcite @xcite . currently, knowledge graph-based rag (kgbased rag) methods have gained increasing attention. knowledge graphs (kgs), which store structured, domain-specific knowledge as triples, are valuable external sources for rag @xcite @xcite . compared to unstructured text, kgs enhance the ability of llms to perform reasoning across entities and relations, improve retrieval accuracy, and reduce hallucinations @xcite . the quality of the subgraph retrieved from the kg as input to the final qa llms is crucial for both reasoning efficiency and answer accuracy of kgbased rag. mainstream methods typically rely on either direct or indirect interaction between llms and the kg to identify answer paths @xcite @xcite , or use graph neural networks (gnns) to propagate embeddings within the graph @xcite . although existing kg-based rag frameworks have shown promising results in knowledge graph question answering (kgqa) tasks, they still face several critical challenges when applied to domains such as space science and utilization, where the problems are complex, reasoning paths are diverse, and the kgs are relatively sparse. firstly, when dealing with semantically complex questions and multi-fact reasoning requirements, many step-bystep search methods over kgs-from an entity to its neighbors-often suffer from significant redundancy and lack of explicit question modeling. this results in the omission of crucial information and inefficiency in reasoning. secondly, gnns operate by iteratively aggregating information from neighboring nodes to update node representations. however, in domain-specific kgs that are relatively sparse, many nodes have very few neighbors, leading to limited aggregation and suboptimal performance. thirdly, in order to enable llms to effectively learn from kgs, some high-accuracy methods @xcite @xcite rely on finetuning large-scale (@xmath0. however, these strategies often consume substantial local computational resources and require prolonged training time, which limits the efficiency and practicality of qa systems. to tackle these challenges, we propose skrag, a skeleton-guided rag framework for kgqa. in particular, skrag fine-tunes a lightweight language model to generate explicit reasoning skeletons that capture core reasoning chains, especially in questions involving multiple question entities, thereby enabling accurate and efficient subgraph retrieval. to ensure these skeletons are executable and structurally aligned with the kg, we introduce the finite state machine (fsm) constraint mechanism, which guides generation by enforcing pathstructure constraints. the subgraph retrieved under the guidance of the reasoning skeleton is then used to prompt a general llm for final answer generation. by leveraging reasoning skeletons, skrag bridges complex reasoning, llm generation, and kg structure, achieving strong performance with improved adaptability and efficiency. our contributions can be summarized as follows: ## methodology in this section, we present the proposed framework, skrag. this framework generates question reasoning skeletons that are faithful to the structure of the kg and make them act as navigational guides to accurately direct the retrieval of subgraphs pertinent to the input question. as illustrated in figure ## domain-specific dataset: ssuqa to support comprehensive evaluation and advance research in kgqa within the domain of space science and utilization, we build a valuable dataset named ssuqa based on the domain-specific kg @xcite . the dataset is constructed through an efficient automated method leveraging type path multi-entity aggregation 1-hop 2-hop 3-hop count 3,500 5,000 5,000 4,000 500 chain-of-thought (cot) @xcite prompting with llms. compared with labor-intensive manual annotation, our method enables scalable and high-quality data generation. the construction process consists of two main stages: triple reasoning paths generation and cot-based prompt construction. triple reasoning paths generation. this stage aims to automatically mine logically valid reasoning paths from the kg to support question generation. we start from each entity and use dfs to extract all 1-hop to 3-hop paths. based on these, we define three types of constraints: ## conclusion in this paper, we propose skrag, a novel kgbased rag framework tailored for kgqa in scenarios involving complex multi-fact questions over relatively sparse kgs, exemplified by but not limited to the domain of space science and utilization. skrag integrates a fine-tuned lightweight llm with the fsm constraint to generate reasoning skeletons for efficient subgraph retrieval. we also introduce ssuqa, a benchmark dataset for the domain of space science and utilization. experimental results demonstrate that skrag outperforms strong baselines on ssuqa and two general kgqa datasets, validating its adaptability and effectiveness across diverse kgqa scenarios.