Generating Complex Questions from Knowledge Graphs with Query Graphs

Question Generation from Knowledge Graphs (KGQG) is a task that aims to generate natural language questions from subgraphs within the given Knowledge Graph. Previous research has discussed numerous KGQG approaches, generating simple and complex questions from triples and queries based on the template-based and Deep Learning-based models. However, the current KGQG research could be identified three unique challenges: determining how to represent the queries, mapping different query structures to different compositional structures in the natural language questions, and generating questions with high language variety. In this paper, we propose a novel framework to conduct the KGQG task, which consists of two stages: query graph construction and graph-to-question generation, to deal with the three identified challenges. Firstly, we propose a query graph structure that specifies the entities and relationships within a SPARQL query addition with the characteristic of each entity in order to explore an appropri-ate query graph representation. We then propose a Graph-to-Sequence (Graph2Seq) model that utilizes Gated Graph Neural Network (GGNN) to encode the constructed query graphs and an attention decoder with a Long Short-Term Memory (LSTM) network to generate natural language questions. To make an objective evaluation, we validate our framework on two compli-cated datasets designed for complex questioning and answering, namely LC-QuAD 2.0 and KQA Pro. Experimental results show a dramatic improvement in our framework compared with the Sequence-to-Sequence (Seq2Seq) baselines. The robustness of our graph structure and the Graph2Seq model in the KGQG tasks are all confirmed, accompanied by a case study to highlight the effectiveness of our proposed framework.