Vol. 1 No. 4 (2026)

Articles

Uncertainty-Calibrated Cross-Domain Relation Alignment with Simplex Embeddings

Published 2026-08-29

  • Elijah Reyes

Elijah Reyes
Department of Pediatrics, Halıcıoğlu School of Data Science and Computing, University of California San Diego, San Diego, California, USA

Data Science and Computational Intelligence, Volume 1, Issue 4, 2026 cover

Abstract

Cross-domain relation alignment has emerged as a fundamental challenge in artificial intelligence, particularly when attempting to transfer structured knowledge between disparate domains characterized by heterogeneous data distributions. Traditional embedding methods often project entities and relations into unconstrained Euclidean spaces, leading to overconfident predictions and severe negative transfer when domain shifts are substantial. To address these critical limitations, this paper introduces a novel framework based on uncertainty-calibrated relation alignment utilizing simplex embeddings. By representing relations not as deterministic point vectors but as probabilistic distributions on a topological simplex, the proposed methodology inherently models both aleatoric and epistemic uncertainties. This probabilistic geometric space naturally bounds the representation, allowing the model to express varying degrees of confidence regarding cross-domain entity interactions. Furthermore, we propose a dynamic calibration mechanism that adjusts alignment penalties based on the quantified uncertainty of the simplex representations, ensuring that highly uncertain relational mappings do not corrupt the target domain structure. Extensive theoretical analysis and simulated experimental evaluations demonstrate that our framework significantly mitigates negative transfer, achieving superior generalization and robust alignment across multiple complex domain scenarios. The results indicate that incorporating uncertainty into topological embedding spaces provides a highly reliable foundation for cross-domain knowledge transfer.

Keywords

Cross-Domain Alignment, Uncertainty Calibration, Simplex Embeddings, Relational Knowledge Transfer, Topological Representation

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