<ul><li>This work focuses on table integration in data lakes to consolidate relevant information.</li><li>The core tasks investigated are pairwise integrability judgment, integrable set discovery, and multi-tuple conflict resolution.</li><li>A binary classifier is trained using a self-supervised adversarial contrastive learning algorithm to address pairwise integrability judgment.</li><li>An innovative in-context learning methodology is introduced to effectively resolve conflicts during multi-tuple integration.</li></ul>

Table Integration in Data Lakes Unleashed: Pairwise Integrability Judgment, Integrable Set Discovery, and Multi-Tuple Conflict Resolution

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