Multi-hop Knowledge Base Question Answering Based on Differentiable Knowledge Graph

Knowledge base question answering(KBQA) is a challenging and popular research direction.Currently,embedding-based methods obtain the answer to a question through implicit reasoning and cannot generate complete reasoning paths.Models based on differentiable knowledge graphs only needs the question-answer pairs as weak supervision signals to generate explainable results.An end-to-end encoder-decoder model based on differentiable knowledge graphs is proposed.The encoder uses multi-head attention mechanism and LSTM to model the fine-grained sequence of questions,generating query vectors that can effectively represent the semantic features of each step of the question.The decoder uses feedforward neural networks to effectively represent the weights of each hop in the entire question.Our model solves the problem of information loss caused by previous coarse-grained and non-sequential modeling methods.The experiments are conducted on five datasets:MetaQA-1hop,MetaQA-2hop,MetaQA-3hop,WebQSP and CWQ,and the model achieves accuracy of 97.5%,100%,100%,77.8% and 51.4%,respectively.The ablation experiment shows that each module contributes to the overall performance improvement of the model.