LLMs have revolutionized knowledge representation and retrieval, but lack the explicit modeling that knowledge ontologies possess. This paper surveys the ways that ontologies and knowledge graphs have been integrated with dense embedding algorithms. All hitherto attempts involve a trade-off between probabilistic and crisp inference. This paper proposes a novel frontier for devising knowledge representation systems that can simultaneously accommodate probabilistic and crisp inference in the same representation. To this effect, the paper proposes neuro-quantum-fuzzy systems as knowledge representation systems that accommodate both classical and contextual inference implemented through quantum-neural networks (QNN).

翻译：大型语言模型彻底改变了知识表示与检索，但缺乏知识本体所具有的显式建模能力。本文综述了本体与知识图谱集成密集嵌入算法的各种方式。迄今为止的所有尝试都涉及概率推理与精确推理之间的权衡。本文提出了一种新的前沿方向，旨在设计能够同时在同一表示中容纳概率推理与精确推理的知识表示系统。为此，本文提出将神经-量子-模糊系统作为知识表示系统，该系统通过量子神经网络同时实现经典推理与上下文推理。