SAVeD: Semantic Aware Version Discovery

TL;DR

SAVeD is a contrastive learning framework that accurately detects dataset versions without relying on metadata, using semantic embeddings and data augmentations to distinguish similar and different datasets.

Contribution

The paper introduces SAVeD, a novel contrastive learning approach employing a custom transformer encoder and data augmentations for semantic-aware dataset version detection without metadata.

Findings

Achieves higher accuracy on unseen tables

Significantly improves separation scores

Outperforms prior state-of-the-art methods

Abstract

Our work introduces SAVeD (Semantically Aware Version Detection), a contrastive learning-based framework for identifying versions of structured datasets without relying on metadata, labels, or integration-based assumptions. SAVeD addresses a common challenge in data science of repeated labor due to a difficulty of similar work or transformations on datasets. SAVeD employs a modified SimCLR pipeline, generating augmented table views through random transformations (e.g., row deletion, encoding perturbations). These views are embedded via a custom transformer encoder and contrasted in latent space to optimize semantic similarity. Our model learns to minimize distances between augmented views of the same dataset and maximize those between unrelated tables. We evaluate performance using validation accuracy and separation, defined respectively as the proportion of correctly classified version/non-version pairs on a hold-out set, and the difference between average similarities of versioned and non-versioned tables (defined by a benchmark, and not provided to the model). Our experiments span five canonical datasets from the Semantic Versioning in Databases Benchmark, and demonstrate substantial gains post-training. SAVeD achieves significantly higher accuracy on completely unseen tables in, and a significant boost in separation scores, confirming its capability to distinguish semantically altered versions. Compared to untrained baselines and prior state-of-the-art dataset-discovery methods like Starmie, our custom encoder achieves competitive or superior results.