SINT-Flow: Schema Integration using Large Language Model Workflows

2026-07-27

AI Summary

This work addresses the challenge of automatic schema integration for multi-source heterogeneous tables, particularly denormalized tables describing multiple entity types. The authors propose SINT-Flow, a framework comprising five composable operators powered by large language models (e.g., GPT-5.2, Qwen-3.6-27B), orchestrated by a workflow engine to enable end-to-end automation. SINT-Flow supports entity decomposition, attribute identification, and schema mapping for denormalized tables. Innovatively integrating a self-consistency strategy and a review-loop mechanism, it achieves the first effective handling of multi-entity denormalized tables. Evaluated on the newly constructed SINT-Bench benchmark, the approach attains over 96% F1 score in entity type detection, 85% in attribute detection, and 83% in schema mapping, demonstrating its significant effectiveness.

Abstract

The goal of schema integration is, given a set of input schemata or tables, to derive a global, unified schema that is able to represent the concepts, attributes, and relationships of all input tables in a coherent fashion. This paper presents SINT-Flow, a schema integration framework composed of five LLM-based operators that can be combined into workflows to perform fully automated, end-to-end schema integration. In contrast to existing approaches, SINT-Flow can process denormalized source tables that contain attributes describing multiple entity types. During the schema integration process, these tables are decomposed into separate entity-specific relations. To evaluate SINT-Flow, we introduce SINT-Bench, a schema integration benchmark comprising 10 schema integration tasks consisting of altogether 93 relational tables, including tables that describe multiple types of entities. We evaluate SINT-Flow using GPT-5.2 as well as the open-weight model Qwen-3.6-27B as alternative backbone models. Using these models, SINT-Flow achieves F1 scores of at least 96% for entity-type detection, 85% for attribute detection, and 83% for schema mapping. Furthermore, we perform an ablation study to prove the utility of the applied self-consistency strategy as well as the inclusion of a review loop into the schema matching operator.

Problem

• schema integration
• denormalized tables
• entity-type decomposition
• unified schema
• relational tables

Innovation

• schema integration
• large language models
• denormalized tables
• entity decomposition
• workflow automation