用AI构建可持续发展目标知识图谱，发现潜在新目标
Large Language Model-Based Knowledge Graph System Construction for Sustainable Development Goals: An AI-Based Speculative Design Perspective
- 结合大模型与检索增强生成，分析269场TED演讲提炼SDG关联
- 发现目标10与16强关联，目标6覆盖不足，提出6个新潜在目标
- 适合政策制定者与跨系统研究者，推动目标演化与创新设计
2000至2015年联合国千年发展目标引导全球议程，后续可持续发展目标（SDGs）采用动态更新机制，每年调整指标。随着2030年临近，进展滞后，亟需创新加速策略。本研究开发了一套AI驱动的知识图谱系统，用于分析SDG间关联、探索潜在新目标，并在线可视化呈现。基于官方SDG文本、爱思唯尔关键词数据集及1,127段TED演讲（2020.01–2024.04）的语料，对2023年269场演讲进行试点分析，结合AI-推测性设计、大语言模型与检索增强生成技术。关键发现包括：(1) 热力图显示目标10与目标16关联性强，目标6覆盖度最低；(2) 在知识图谱中模拟随时间演化的对话，揭示新核心节点，表明更丰富数据可促进思维发散与目标清晰化；(3) 提出六个潜在新目标，聚焦公平、韧性与技术驱动包容。该推测性-AI框架为政策制定提供新视角，并为未来多模态与跨系统SDG应用奠定基础。
原文摘要 · Abstract (English)
From 2000 to 2015, the UN's Millennium Development Goals guided global priorities. The subsequent Sustainable Development Goals (SDGs) adopted a more dynamic approach, with annual indicator updates. As 2030 nears and progress lags, innovative acceleration strategies are critical. This study develops an AI-powered knowledge graph system to analyze SDG interconnections, discover potential new goals, and visualize them online. Using official SDG texts, Elsevier's keyword dataset, and 1,127 TED Talk transcripts (2020.01-2024.04), a pilot on 269 talks from 2023 applies AI-speculative design, large language models, and retrieval-augmented generation. Key findings include: (1) Heatmap analysis reveals strong associations between Goal 10 and Goal 16, and minimal coverage of Goal 6. (2) In the knowledge graph, simulated dialogue over time reveals new central nodes, showing how richer data supports divergent thinking and goal clarity. (3) Six potential new goals are proposed, centered on equity, resilience, and technology-driven inclusion. This speculative-AI framework offers fresh insights for policymakers and lays groundwork for future multimodal and cross-system SDG applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。