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.

“Pre-trained language models (PLMs) have been widely used in entity and relation extractionmethods in recent years. However, due to the semantic gap between general-domain text usedfor pre-training and domain-specific text, these methods encounter semantic redundancy anddomain semantics insufficiency when it comes to domain-specific tasks. To mitigate this issue,we propose a low-cost and effective knowledge-enhanced method to facilitate domain-specificsemantics modeling in joint entity and relation extraction. Precisely, we use ontology and entitytype descriptions as domain knowledge sources, which are encoded and incorporated into thedownstream entity and relation extraction model to improve its understanding of domain-specificinformation. We construct a dataset called SSUIE-RE for Chinese entity and relation extractionin space science and utilization domain of China Manned Space Engineering, which contains awealth of domain-specific knowledge. The experimental results on SSUIE-RE demonstrate theeffectiveness of our method, achieving a 1.4% absolute improvement in relation F1 score overprevious best approach. Introduction”