Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) in Knowledge Graph Question Answering (KGQA) by retrieving reasoning paths from external Knowledge Graphs (KGs). However, the practicality of existing methods is often limited by their reliance on iterative retrieval, where the number of required LLM calls increases with retrieval depth, leading to expensive interaction costs. To address this, we propose Plan-guided Schema-level Reasoning (PSR), a training-free and efficient RAG framework for KGQA that requires only three LLM calls. First, PSR generates a retrieval plan from the input question (the first LLM call). It then performs Plan-Regularized Monte Carlo Tree Search to extract a compact set of schema-level paths from the KG, which are subsequently filtered by the LLM (the second LLM call). Finally, PSR grounds the chosen schema-level paths into instance-level reasoning paths to augment the LLM’s answer generation (the third LLM call). Experimental results on three datasets demonstrate the high efficiency and competitive accuracy of PSR.

While question answering over knowledge bases (KBQA) has shown progress in addressing factoid questions, KBQA with numerical reasoning remains relatively unexplored. In this paper, we focus on the complex numerical reasoning in KBQA, and propose a new task, NR-KBQA, which necessitates the ability to perform both multi-hop reasoning and numerical reasoning. We also design a logic form in Python format called PyQL to represent the reasoning process of numerical reasoning questions. To facilitate the development of NR-KBQA, we present a large NR-KBQA dataset called MarkQA, which is automatically constructed by a small set of seeds. Each question in MarkQA is annotated with its corresponding SPARQL query, alongside the step-by-step reasoning path in the QDMR format and PyQL program. Experimental results of some state-of-the-art QA methods performed on the MarkQA dataset show that complex numerical reasoning in KBQA faces great challenges.