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The Vera C. Rubin Observatory has begun initial observations, already discovering rapidly spinning asteroids, supernovas, and a potential interstellar visitor, heralding a new era of big-data astronomy.
This paper introduces a knowledge-based approach using knowledge graph embeddings to automatically assess big data quality by predicting missing edges between context representations and quality rules, outperforming traditional matching methods.