Large Language Model-Based Knowledge Graph System Construction for Sustainable Development Goals: An AI-Based Speculative Design Perspective

• URL: http://arxiv.org/abs/2504.12309v2
• Date: Fri, 18 Apr 2025 05:38:41 GMT
• Title: Large Language Model-Based Knowledge Graph System Construction for Sustainable Development Goals: An AI-Based Speculative Design Perspective
• Authors: Yi-De Lin, Guan-Ze Liao,
• Abstract summary: This study develops an AI-powered knowledge graph system to analyze Sustainable Development Goals.<n>It discovers potential new goals, and visualizes them online.<n>Six potential new goals are proposed, centered on equity, resilience, and technology-driven inclusion.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook [85.43403500874889]
  Retrieval-augmented generation (RAG) has emerged as a pivotal technique in artificial intelligence (AI) Recent advancements in RAG for embodied AI, with a particular focus on applications in planning, task execution, multimodal perception, interaction, and specialized domains.
  arXiv  Detail & Related papers  (2025-03-23T10:33:28Z)
• Graph Foundation Models for Recommendation: A Comprehensive Survey [55.70529188101446]
  Large language models (LLMs) are designed to process and comprehend natural language, making both approaches highly effective and widely adopted.<n>Recent research has focused on graph foundation models (GFMs)<n>GFMs integrate the strengths of GNNs and LLMs to model complex RS problems more efficiently by leveraging the graph-based structure of user-item relationships alongside textual understanding.
  arXiv  Detail & Related papers  (2025-02-12T12:13:51Z)
• Capturing research literature attitude towards Sustainable Development Goals: an LLM-based topic modeling approach [0.7806050661713976]
  The Sustainable Development Goals were formulated by the United Nations in 2015 to address these global challenges by 2030. Natural language processing techniques can help uncover discussions on SDGs within research literature. We propose a completely automated pipeline to fetch content from the Scopus database and prepare datasets dedicated to five groups of SDGs.
  arXiv  Detail & Related papers  (2024-11-05T09:37:23Z)
• Sustainable Visions: Unsupervised Machine Learning Insights on Global Development Goals [0.3764231189632788]
  The 2030 Agenda for Sustainable Development outlines 17 goals for countries of the world to address global challenges in their development.<n>We have used a novel data-driven methodology to analyze time-series data for over 20 years (2000-2022) from 107 countries using unsupervised machine learning (ML) techniques.<n>Our findings show that progress toward the SDGs is heavily influenced by geographical, cultural and socioeconomic factors.
  arXiv  Detail & Related papers  (2024-09-19T03:10:49Z)
• A Survey on Vision-Language-Action Models for Embodied AI [71.16123093739932]
  Embodied AI is widely recognized as a key element of artificial general intelligence.<n>A new category of multimodal models has emerged to address language-conditioned robotic tasks in embodied AI.<n>We present the first survey on vision-language-action models for embodied AI.
  arXiv  Detail & Related papers  (2024-05-23T01:43:54Z)
• The 2nd FutureDial Challenge: Dialog Systems with Retrieval Augmented Generation (FutureDial-RAG) [23.849336345191556]
  The challenge builds upon the MobileCS2 dataset, a real-life customer service datasets with nearly 3000 high-quality dialogs. We define two tasks, track 1 for knowledge retrieval and track 2 for response generation, which are core research questions in dialog systems with RAG. We build baseline systems for the two tracks and design metrics to measure whether the systems can perform accurate retrieval and generate informative and coherent response.
  arXiv  Detail & Related papers  (2024-05-21T07:35:21Z)
• Deep learning for dynamic graphs: models and benchmarks [16.851689741256912]
  Recent progress in research on Deep Graph Networks (DGNs) has led to a maturation of the domain of learning on graphs. Despite the growth of this research field, there are still important challenges that are yet unsolved.
  arXiv  Detail & Related papers  (2023-07-12T12:02:36Z)
• A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT [63.58711128819828]
  ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC) The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.
  arXiv  Detail & Related papers  (2023-03-07T20:36:13Z)
• SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning [63.192289553021816]
  Progress toward the United Nations Sustainable Development Goals has been hindered by a lack of data on key environmental and socioeconomic indicators. Recent advances in machine learning have made it possible to utilize abundant, frequently-updated, and globally available data, such as from satellites or social media. In this paper, we introduce SustainBench, a collection of 15 benchmark tasks across 7 SDGs.
  arXiv  Detail & Related papers  (2021-11-08T18:59:04Z)
• Graph Learning based Recommender Systems: A Review [111.43249652335555]
  Graph Learning based Recommender Systems (GLRS) employ advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics for recommendations. We provide a systematic review of GLRS, by discussing how they extract important knowledge from graph-based representations to improve the accuracy, reliability and explainability of the recommendations.
  arXiv  Detail & Related papers  (2021-05-13T14:50:45Z)
• Benchmarking Graph Neural Networks [75.42159546060509]
  Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. For any successful field to become mainstream and reliable, benchmarks must be developed to quantify progress. GitHub repository has reached 1,800 stars and 339 forks, which demonstrates the utility of the proposed open-source framework.
  arXiv  Detail & Related papers  (2020-03-02T15:58:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.