Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning: How can the output of AI systems be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives.

翻译：知识图谱（KGs）已成为支撑谷歌、沃尔玛、爱彼迎等大型企业智能决策及各类人工智能（AI）服务的基础平台。知识图谱通过提供数据上下文与语义信息，补充机器学习（ML）算法，增强其推理与问答能力。当前，将知识图谱与神经学习（例如大型语言模型LLMs）相结合（即神经符号AI）是活跃的研究领域。尽管基于知识图谱的人工智能能带来诸多益处，但其在在线服务中日益普及可能引发公民丧失自我决定这一根本性社会问题。人类对这些（常为集中式的）技术的依赖程度越高，自主规划人生轨迹的能力就越弱。为应对此威胁，部分区域（如欧盟）正推动制定《人工智能法案》等监管法规。此类法规对技术开发者提出要求，从而引发关键问题：如何信任AI系统的输出？如何确保驱动这些系统运行的数据及其内部机制具备透明度？如何让AI对其决策行为承担问责？本文对支持自我决定的基于知识图谱的人工智能的基础议题与研究支柱进行概念化阐释。基于该概念框架，我们通过真实场景实例分析公民自我决定面临的挑战与机遇。最终提出旨在达成预期目标的研究议程。