Path-aware Multi-hop Question Answering Over Knowledge Graph Embedding

Abstract

Question answering over knowledge graph (KGQA) aims at answering questions posed over the knowledge graph (KG). Multi-hop KGQA requires multi-hop reasoning on KG to achieve the correct answer. Unfortunately, KGs are usually incomplete with many missing links, which poses additional challenges to KGQA. KG embedding-based KGQA methods have recently been proposed as a way to overcome this limitation. However, existing KG embedding-based KGQA methods fail to take full advantage of semantic correlations between questions and paths. Furthermore, their inference process is not easily explainable. To address these challenges, we propose a novel path-aware multi-hop KGQA model (PA-KGQA), which can fully capture semantic correlations between the paths and the questions in a feature-interactive manner. Specifically, we introduce a case-enhanced path retriever to evaluate the importance of paths between topic entities and candidate answer entities, and then propose an interactive convolutional neural network (ICNN) to model the interactions between paths and questions for mining richer correlation features. Experiments show that PA-KGQA achieves state-of-the-art results on multiple benchmark datasets and is explainable.