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

TL;DR

This work tackles data-lake table integration via three core tasks after schema alignment: pairwise integrability judgment, integrable set discovery, and multi-tuple conflict resolution. It introduces SSACL to train a pairwise integrability classifier under limited labeled data, and tackles integrable set discovery with both maximal-clique (Bron-Kerbosch) and relaxed community-detection approaches, while proposing In-context Learning for Conflict Resolution (ICLCR) that uses LLMs with demonstration compression and selection. Benchmarks are created by injecting semantic equivalence, typographical errors, and conflicts, and the results show SSACL achieving a relative improvement of about 4.2% in F1 for pairwise integrability and ICLCR achieving about 18.9% accuracy improvement for multi-tuple conflict resolution, with Graph Neural Networks providing the best results for integrable-set discovery. Overall, the work reduces labeled-data requirements and demonstrates scalable, knowledge-driven table integration for data lakes, with public benchmarks and open-source code.

Abstract

Table integration aims to create a comprehensive table by consolidating tuples containing relevant information. In this work, we investigate the challenge of integrating multiple tables from a data lake, focusing on three core tasks: 1) pairwise integrability judgment, which determines whether a tuple pair is integrable, accounting for any occurrences of semantic equivalence or typographical errors; 2) integrable set discovery, which identifies all integrable sets in a table based on pairwise integrability judgments established in the first task; 3) multi-tuple conflict resolution, which resolves conflicts between multiple tuples during integration. To this end, we train a binary classifier to address the task of pairwise integrability judgment. Given the scarcity of labeled data in data lakes, we propose a self-supervised adversarial contrastive learning algorithm to perform classification, which incorporates data augmentation methods and adversarial examples to autonomously generate new training data. Upon the output of pairwise integrability judgment, each integrable set can be considered as a community, a densely connected sub-graph where nodes and edges correspond to tuples in the table and their pairwise integrability respectively, we proceed to investigate various community detection algorithms to address the integrable set discovery objective. Moving forward to tackle multi-tuple conflict resolution, we introduce an innovative in-context learning methodology. This approach capitalizes on the knowledge embedded within large language models (LLMs) to effectively resolve conflicts that arise when integrating multiple tuples. Notably, our method minimizes the need for annotated data, making it particularly suited for scenarios where labeled datasets are scarce.