Robust Table Integration in Data Lakes

2024-11-30

arXiv.org

AI Summary

This work addresses robust integration of heterogeneous tables in data lakes, targeting three core tasks: (1) pairwise tuple integrability determination under semantic equivalence or spelling errors; (2) automatic discovery of integrable tuple sets; and (3) multi-tuple conflict resolution. To mitigate severe label scarcity, we propose a self-supervised adversarial contrastive learning framework. We model integrable tuple sets as graph communities and adapt an enhanced community detection algorithm for this purpose. Furthermore, we introduce the first large language model (LLM)-based in-context learning approach for conflict resolution. Extensive evaluation on newly constructed Real and Join benchmarks demonstrates that our method substantially reduces annotation dependency while achieving superior integration accuracy and cross-domain generalization compared to state-of-the-art baselines.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

In this paper, we investigate the challenge of integrating tables from data lakes, focusing on three core tasks: 1) pairwise integrability judgment, which determines whether a tuple pair in a table is integrable, accounting for any occurrences of semantic equivalence or typographical errors; 2) integrable set discovery, which aims to identify all integrable sets in a table based on pairwise integrability judgments established in the first task; 3) multi-tuple conflict resolution, which resolves conflicts among multiple tuples during integration. We train a binary classifier to address the task of pairwise integrability judgment. Given the scarcity of labeled data, 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 is 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 novel in-context learning methodology. This approach capitalizes on the knowledge embedded within pretrained large language models to effectively resolve conflicts that arise when integrating multiple tuples. Notably, our method minimizes the need for annotated data. Since no suitable test collections are available for our tasks, we develop our own benchmarks using two real-word dataset repositories: Real and Join. We conduct extensive experiments on these benchmarks to validate the robustness and applicability of our methodologies in the context of integrating tables within data lakes.

Problem

• Determine tuple integrability considering semantic and typographical errors
• Discover integrable sets using pairwise judgments and community detection
• Resolve multi-tuple conflicts via LLM-based in-context learning

Innovation

• Self-supervised adversarial contrastive learning for classification
• Community detection algorithms for integrable set discovery
• In-context learning with LLMs for conflict resolution