Existing embedded multi-hop Question Answering over Knowledge Graph (KGQA) methods attempted to handle Knowledge Graph (KG) sparsity using Knowledge Graph Embedding (KGE) to improve KGQA. However, they almost ignore the intermediate path reasoning process of answer prediction, do not consider the information interaction between the question and the KG, and rarely consider the problem that the triple scoring reasoning mechanism is inadequate in extracting deep features. To address the above issues, this paper proposes Global and Item-by-item Reasoning Fusion-based Multi-hop KGQA (GIRFM-KGQA). In global reasoning, a convolutional attention reasoning mechanism is proposed and fused with the triple scoring reasoning mechanism to jointly implement global reasoning, thus enhancing the long-chain reasoning ability of the global reasoning model. In item-by-item reasoning, the reasoning path is formed by serially predicting relations, and then the answer is predicted, which effectively solves the problem that the embedded multi-hop KGQA method lacks the intermediate path reasoning ability. In addition, we introduce an information interaction method between the question and the KG to improve the accuracy of the answer prediction. Finally, we merge the global reasoning score with the item-by-item reasoning score to jointly predict the answer. Our model, compared to the baseline model (EmbedKGQA), achieves an accuracy improvement of 0.5% and 2.7% on two-hop questions, and 6.2% and 4.6% on three-hop questions for the MetaQA_Full and MetaQA_Half datasets, and 1.7% on the WebQuestionSP dataset, respectively. The experimental results show that the proposed model can effectively improve the accuracy of the multi-hop KGQA model and enhance the interpretability of the model. We have made our model’s source code available at github: https://github.com/feixiongfeixiong/GIRFM.

现有的嵌入式多跳知识图问答（KGQA）方法尝试使用知识图嵌入（KGE）来处理知识图（KG）稀疏性以改进KGQA。然而，他们几乎忽略了答案预测的中间路径推理过程，没有考虑问题与知识图谱之间的信息交互，也很少考虑三重评分推理机制在提取深层特征方面不足的问题。为了解决上述问题，本文提出了基于全局和逐项推理融合的多跳KGQA（GIRFM-KGQA）。在全局推理方面，提出了卷积注意力推理机制，并与三重评分推理机制融合，共同实现全局推理，从而增强了全局推理模型的长链推理能力。在逐项推理中，通过串行预测关系形成推理路径，然后预测答案，有效解决了嵌入式多跳KGQA方法缺乏中间路径推理能力的问题。此外，我们引入了问题和知识图谱之间的信息交互方法，以提高答案预测的准确性。最后，我们将全局推理得分与逐项推理得分合并以共同预测答案。与基线模型 (EmbedKGQA) 相比，我们的模型在 MetaQA_Full 和 MetaQA_Half 数据集上的两跳问题上实现了 0.5% 和 2.7% 的准确度提高，在三跳问题上实现了 6.2% 和 4.6% 的准确度提高，在 MetaQA_Full 和 MetaQA_Half 数据集上实现了 1.7% 的准确度提升。分别是 WebQuestionSP 数据集。实验结果表明，所提模型能够有效提高多跳KGQA模型的准确性，增强模型的可解释性我们已在 github 上提供了模型的源代码： https: //github.com/feixiongfeixiong/GIRFM。