GNN2R: Weakly-Supervised Rationale-Providing Question Answering over Knowledge Graphs

Despite the rapid progress of large language models (LLMs), knowledge graph-based question answering (KGQA) remains essential for producing verifiable and hallucination-resistant answers in many real-world settings where answer trustworthiness and computational efficiency are highly valued. However, most existing KGQA methods provide only final answers in the form of KG entities. Without explicit explanations -- ideally in the form of intermediate reasoning process over relevant KG triples, the QA results are difficult to inspect and interpret. Moreover, this limitation prevents the rich and verifiable knowledge encoded in KGs, which is a key advantage of KGQA over LLMs, from being fully leveraged. However, addressing this issue remains highly challenging due to the lack of annotated intermediate reasoning process and the requirement of high efficiency in KGQA. In this paper, we propose a novel Graph Neural Network-based Two-Step Reasoning method (GNN2R) that can efficiently retrieve both final answers and corresponding reasoning subgraphs as verifiable rationales, using only weak supervision from widely-available final answer annotations. We extensively evaluated GNN2R and demonstrated that GNN2R substantially outperforms existing state-of-the-art KGQA methods in terms of effectiveness, efficiency, and the quality of generated explanations. The complete code and pre-trained models are available at https://github.com/ruijie-wang-uzh/GNN2R.