Abstract
Multi-hop Knowledge Graph Question Answering (KGQA) aims to find the answer entity via a reasoning path consisting of multiple fact triples in the knowledge graph (KG). Most of end-to-end KGQA approaches only pay attention to answering one-hop simple questions and lack scalability and interpretability. Meanwhile, since the high cost for data annotations, the lack of intermediate supervision signals becomes a major challenge. To address these challenges, we propose a policy-based reinforcement learning model called RPGQA which converts the task of KGQA to a reasoning path generation task in the KG. Firstly, in order to improve the interpretability of the model, the agent in our model learns an effective policy to reason a path to the answer entity as the evidence for the question. Secondly, we design an algorithm for entity disambiguation during entity linking. After that, the topic entity in the question can be linked as the beginning of the reasoning path. Furthermore, we propose a reward shaping policy consisting of three parts to enhance intermediate supervision signals, which alleviates the problem of reward delay and sparsity of reward. Extensive experiments on multiple benchmark datasets have demonstrated the effectiveness of our model. RPGQA outperforms most of the state-of-art baselines on the multi-hop KGQA task.
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Xiang, Y. et al. (2023). Reasoning Path Generation for Answering Multi-hop Questions Over Knowledge Graph. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13422. Springer, Cham. https://doi.org/10.1007/978-3-031-25198-6_16
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