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.

知识图谱已经成为推动智能决策和各类人工智能服务的基础平台，但其与大型语言模型等脑神经学习技术的整合将带来公民自主权的丧失问题，因此本文概念化了支持基于知识图谱的人工智能自主权的基本主题和研究支柱，并在真实场景中分析了公民自主权的挑战和机遇，最终提出了一个研究议程以实现建议的目标。