Extending Ontologies: From Dense Embeddings to Hybrid Quantum-Fuzzy Systems
For researchers in knowledge representation, this paper proposes a conceptual framework but lacks concrete results or evaluation.
This survey reviews ontology-embedding integration methods and proposes neuro-quantum-fuzzy systems that combine probabilistic and crisp inference via quantum-neural networks, addressing the trade-off in existing approaches.
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).