GenBoost: Generative Modeling and Boosted Learning for Multi-hop Question Answering over Incomplete Knowledge Graphs

Multi-hop question answering over incomplete knowledge graphs involves iteratively reasoning on the provided question and graph to find answers, while also tackling the inherent sparsity problem in the graph. Walk-based methods transform the reasoning process into a graph traversal task; however, they encounter challenges in convergence and stability due to the extensive action space and sensitivity to missing triples. On the other hand, embedding-based methods address the problem of missing triples but compromise interpretability in answer selection because of their black-box nature. We present GenBoost, a Generate-then-Boost framework for generative reasoning. By transforming the question-answering task into an inference path generation task, GenBoost effectively addresses existing limitations and offers a more efficient and interpretable approach for answer selection. GenBoost possesses two key features: (1) The reasoning procedure does not explicitly rely on the existing triples in the knowledge graph. By combining graph traversal and link prediction, our approach mitigates the impact of knowledge graph incompleteness. (2) Each entity in the reasoning path is generated autoregressively, providing insights into the decision-making process during multi-hop reasoning and enhancing interpretability. Extensive experiments conducted on incomplete knowledge graphs have demonstrated the effectiveness of our approach.