The authors replace discontinuous precedence and frontier constraints in a partial-order model with smooth surrogates, producing a continuous posterior that supports gradient MCMC and variational inference while recovering the hard model in the limit.
Probabilistic embedding of knowledge graphs with box lattice measures
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Polaris learns hierarchical concepts via coupled orbital polar embeddings on hyperspheres that separate meaning from structure using tangent projections, exponential maps, and asymmetric objectives, yielding up to 19-point gains in top-K retrieval.
GNNs with ontology-derived semantic loss create hierarchy-aware KG embeddings that predict yeast double gene knockout phenotypes with mean R²=0.360 (improved to 0.377 with semantic loss), outperforming baselines, generalizing to triple knockouts, and supporting experimental hypothesis validation.
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A Differentiable Bayesian Relaxation for Latent Partial-Order Inference
The authors replace discontinuous precedence and frontier constraints in a partial-order model with smooth surrogates, producing a continuous posterior that supports gradient MCMC and variational inference while recovering the hard model in the limit.