Dynamic knowledge graph reasoning based on deep reinforcement learning

Edge-Aware Graph Neural Network for Multi-Hop Path Reasoning over Knowledge Base

Multi-hop path reasoning over knowledge base aims at finding answer entities for an input question by walking along a path of triples from graph structure data, which is a crucial branch in the knowledge base question answering (KBQA) research field. Previous studies rely on deep neural networks to simulate the way humans solve multi-hop questions, which do not consider the latent relation information contained in connected edges, and lack of measuring the correlation between specific relations and the input question. To address these challenges, we propose an edge-aware graph neural network for multi-hop path reasoning task. First, a query node is directly added to the candidate subgraph retrieved from the knowledge base, which constructs what we term a query graph. This graph construction strategy makes it possible to enhance the information flow between the question and the nodes for the subsequent message passing steps. Second, question-related information contained in the relations is added to the entity node representations during graph updating; meanwhile, the relation representations are updated. Finally, the attention mechanism is used to weight the contribution from neighbor nodes so that only the information of neighbor nodes related to the query can be injected into new node representations. Experimental results on MetaQA and PathQuestion-Large (PQL) benchmarks demonstrate that the proposed model achieves higher Hit@1 and F1 scores than the baseline methods by a large margin. Moreover, ablation studies show that both the graph construction and the graph update algorithm contribute to performance improvement.

Analysis of Knowledge Graph Path Reasoning Based on Variational Reasoning

Knowledge graph (KG) reasoning improves the perception ability of graph structure features, improving model accuracy and enhancing model learning and reasoning capabilities. This paper proposes a new GraphDIVA model based on the variational reasoning divergent autoencoder (DIVA) model. The network structures and calculation processes of the models are analyzed. The GraphSAGE algorithm is introduced into the path reasoning module to solve the inability of the original model to perceive the features of the graph structure, which leads to a decline in the accuracy rate. Hence, GraphDIVA can achieve a higher accuracy rate with fewer learning iterations. The experiments show the efficiency and effectiveness of our model and proves that our method has a better effect on the accuracy rate and training difficulty than the baseline model on the FB15k-237 and NELL-995 benchmark datasets.