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From co-occurrence to coherence: Quantum-informed representation learning for knowledge graph completion
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AI
DOI：10.1016/j.knosys.2026.115408.png)
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Abstract
En 中文
Knowledge graph completion (KGC) aims to infer missing facts by learning latent semantic patterns from observed triples. While existing methods learn superficial semantic co-occurrence through classical probabilistic frameworks, they struggle to capture non-classical semantic properties such as entanglements that govern intrinsic correlations between semantics. These entanglements, critical for disambiguating contextual semantics, cannot be represented in classical probabilistic spaces which lack mathematical tools to represent quantum-like properties. We address this gap with QIKGC, a quantum-informed KGC framework that (i) embeds entity semantics into Hilbert space to explicitly model entanglement, (ii) leverages matrix product states to approximate high-dimensional semantic structures with polynomial complexity, and (iii) treats relations as quantum measurements followed by tomography-based scoring to obtain context-specific entity representations. This is, to our knowledge, the first KGC model that unifies semantic entanglement modeling with trainable quantum operators while remaining efficient on classical hardware. Extensive experiments on four benchmarks demonstrate clear quantitative gains, for example increasing MRR from 0.511 to 0.537 on WN18RR and from 0.904 to 0.926 on Kinship over the best baselines.