Progressive multi-hop reasoning for question answering over knowledge graphs

Question answering over knowledge graphs (QA-KG) is a challenging task due to the complexity of natural language questions. Competitive methods proposed for the task utilize reinforcement learning approaches to perform multi-hop reasoning over questions. These methods often use weak supervision signals from answer entities and manually set rewards to guide the model during the reasoning process. However, these methods open the risk of spurious trajectories that incidentally lead to answer entities, which eventually leads to negative supervision. Unfortunately, these trajectories are not reflected in the existing evaluation indicators to validate the reasoning process. As a solution, we propose a Progressive Multi-hop Reasoning (PMR) model for QA-KG. The model is composed of an encoder, selector, linker and checker, which learns to hop progressively in the reasoning process by leveraging strong supervision signals from SPARQL parsing results. Extensive experiments show that our model achieves state-of-the-art results on two mainstream datasets. More importantly, our results far exceeds methods based on reinforcement learning when matching the intermediate relationship path.