A reasoning enhance network for muti-relation question answering

Multi-relation Question Answering is an important task of knowledge base over question answering (KBQA), multi-relation means that the question contains multiple relations and entity information, so it needs to use the fact triples in the knowledge base to analyze and reasoning the question in more detail. In this paper, we propose a novel model called Reasoning Enhance Network that uses context information, enhance the accuracy of relation and entity predicted in each hop. The model obtains the relation by analyzing the context information before each hop start, and then reasons the answer by the previous information; update question representation and reasoning state through predicted relation and entity, then promote the next hop reasoning starts. Our experiments clearly show that our method achieves good results on four datasets. Also, since we use attention mechanisms, our method offers better interpretability.