aiqbench analysis
This paper addresses a key limitation in current knowledge systems by proposing neuro-quantum-fuzzy architectures that unify probabilistic and crisp inference within a single representation. By integrating quantum-neural networks with classical logic, the approach aims to resolve the inherent trade-offs found in existing ontology-embedding hybrids. The work represents a theoretical advancement in hybrid quantum-classical AI, though practical implementation remains in the research phase.
Extending Ontologies: From Dense Embeddings to Hybrid Quantum-Fuzzy Systems
arXiv:2606.08658v1 Announce Type: new Abstract: 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).
Abstract: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).
Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
I.2.7; I.2.4; I.2.6
arXiv:2606.08658 [cs.AI]
(or arXiv:2606.08658v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.08658 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 193 words