Abstract:Due to the complexity of multi-hop KBQA, most existing works capture a wider range of higher-order neighbour information via multi-layer GNNs. This approach combines multi-order information that sacrifices node discriminativeness to obtain more global information. Furthermore, it suffers from the over-smoothing problem, since fusing the multi-order information ignores the confidence of the neighbours, as neighbours closer to the node have the higher confidence. Another problem with multi-hop KBQA is that many datasets commonly do not provide the supervision information of intermediate paths. Thus, the weak-supervision problem usually makes the model lack effectual guidance information when conducting path reasonings, which results in reduced model reasoning ability. Aiming to solve the above problems, an approach of Multi-view Semantic Reasoning Networks (Multi-view SRNs) which utilizes information from both global and local views to jointly perform the reasoning was proposed in this paper. Namely, the global view information refers to the multi-order neighbour information of the node, which provides more numbers of crucial evidence for the reasoning, while the local view barely focuses on the first-order neighbour information of the node, which makes the node representation more discriminative, thereby alleviating the over-smoothing problem of the global information. Moreover, the original question was decomposed into multiple sub-questions as the guiding information for intermediate path reasoning. Then, an innovative loss function based on the uniformity and consistency of the semantic composition of the question was designed to improve the question decomposition quality, which promotes the ability of the model for intermediate path reasoning. The extensive experimental results on three benchmark datasets convincingly demonstrate that multi-view semantic information can provide a more comprehensive evidence for the reasoning, and the proposed method of decomposing the question into sub-questions is able to increase the intermediate path reasoning accuracy.