提出融合量子与模糊逻辑的新型知识表示系统，兼顾精确与概率推理。
Extending Ontologies: From Dense Embeddings to Hybrid Quantum-Fuzzy Systems
- 用量子神经网络构建混合推理框架
- 实现同一表示中同时支持确定性与概率推理
- 适合需要多模态推理的智能系统研究者
大语言模型革新了知识表示与检索，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与密集嵌入算法的整合方式。迄今为止的所有尝试均在概率推理与确定性推理之间存在权衡。本文提出一个新方向：设计可同时容纳概率与确定性推理的知识表示系统。为此，提出神经-量子-模糊系统，通过量子神经网络（QNN）实现经典与上下文感知推理。
原文摘要 · Abstract (English)
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).
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