{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/toward-subgraph-guided-knowledge-graph","title":"Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks","arxiv_id":"2004.06015","date":"2020-04-13","proceeding":null,"authors":["Yu Chen","Lingfei Wu","Mohammed J. Zaki"],"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.","url_abs":"https://arxiv.org/abs/2004.06015v4","url_pdf":"https://arxiv.org/pdf/2004.06015v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"toward-subgraph-guided-knowledge-graph","repo_url":"https://github.com/hugochan/Graph2Seq-for-KGQG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"kg-to-text","task_name":"KG-to-Text Generation"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"question-generation","task_name":"Question Generation"},{"task_slug":"question-generation","task_name":"Question-Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/kg-to-text-generation-on-pathquestion","task":"KG-to-Text Generation","dataset":"PathQuestion","model":"SOTA-NPT","rank_in_archive_order":3,"of":5,"metrics":{"BLEU":"61.48","METEOR":"44.57","ROUGE":"77.72"},"uses_additional_data":false},{"leaderboard":"/sota/kg-to-text-generation-on-webquestions","task":"KG-to-Text Generation","dataset":"WebQuestions","model":"SOTA-NPT","rank_in_archive_order":3,"of":5,"metrics":{"BLEU":"29.45","METEOR":"30.96","ROUGE":"55.45"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.06015","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.06015"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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