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

Abstract

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.

Retrieval-Augmented Knowledge Graph Reasoning for Commonsense Question Answering

“…When noisy nodes are not discarded in a timely manner, the performance of the model in predicting answers will be negatively impacted [13,14]. On the other hand, in previous works [15,16], language models and knowledge graph models have existed as independent components, which resulted in a missing relationship between the question and graph entities. The limited interaction between language models and knowledge graph models is a major issue, which causes models to struggle with understanding complex questionknowledge relations [17][18][19].…”