IKBQA-R1: Reinforcing Large Language Models for Question Answering over Incomplete Knowledge Graphs

Knowledge Base Question Answering (KBQA) aims to answer natural language questions through reasoning over structured knowledge graphs (KGs), offering explicit and traceable inference that is valuable in hallucination-sensitive domains. However, real-world KGs are inherently incomplete, causing even semantically correct logical forms to return empty or incomplete answers when required evidence is missing. Existing approaches mitigate KG incompleteness by generating missing facts from LLMs or incorporating retrieved text into multi-step reasoning, but such textual evidence is often fragmented, transient, and difficult to accumulate and reuse across multiple reasoning steps. To address these limitations, we propose $\text{IKBQA-R1}$, an interactive reinforcement learning framework that formulates incomplete KBQA as a multi-turn decision process and expands the structured action space with \texttt{Search\\_Web} and $\text{Combine}$ actions for evidence integration into the logical form. To mitigate the cold-start problem, we first initialize the policy through Referenced Rejection Sampling and hint-free multi-turn supervised fine-tuning; we then introduce $\text{action-aware credit assignment}$ into GRPO, aligning generated actions with gold action anchors and projecting local action quality into bounded token-level credit adjustments. We further construct the IKBQA benchmarks WebQSP-IKG and GraphQ-IKG from the widely used WebQSP and GraphQuestions datasets to support multi-turn policy learning and evaluation under controlled KG incompleteness. Experimental results show that IKBQA-R1 outperforms the baseline on WebQSP-IKG by 8.1% in relative F1 improvement, while achieving 32.7 and 16.1 F1-point gains over gold execution without external evidence on WebQSP-IKG and GraphQ-IKG, respectively.