Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks

• URL: http://arxiv.org/abs/2004.06015v4
• Date: Mon, 1 May 2023 03:15:49 GMT
• Title: Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks
• Authors: Yu Chen, Lingfei Wu and Mohammed J. Zaki
• Abstract summary: Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers.
• Score: 53.58077686470096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. Previous works mostly focus on a simple setting which is to generate questions from a single KG triple. In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers. In addition, most of previous works built on either RNN-based or Transformer based models to encode a linearized KG sugraph, which totally discards the explicit structure information of a KG subgraph. To address this issue, we propose to apply a bidirectional Graph2Seq model to encode the KG subgraph. Furthermore, we enhance our RNN decoder with node-level copying mechanism to allow directly copying node attributes from the KG subgraph to the output question. Both automatic and human evaluation results demonstrate that our model achieves new state-of-the-art scores, outperforming existing methods by a significant margin on two QG benchmarks. Experimental results also show that our QG model can consistently benefit the Question Answering (QA) task as a mean of data augmentation.

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