Global and item-by-item reasoning fusion-based multi-hop KGQA

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.