用大模型工作流自动整合多源异构数据表，支持混合实体类型。
SINT-Flow: Schema Integration using Large Language Model Workflows
- 基于大模型构建五步工作流，自动分解混合实体的冗余数据表
- 在93张表上实现96%实体类型识别、85%属性识别和83%模式映射准确率
- 适合需要自动化元数据整合的数据工程与知识库构建场景
模式整合的目标是，给定一组输入模式或数据表，生成一个全局统一的模式，能够以连贯方式表示所有输入表中的概念、属性和关系。本文提出SINT-Flow，一个由五个基于大模型的算子组成的模式整合框架，可组合成工作流实现完全自动化、端到端的模式整合。与现有方法不同，SINT-Flow能处理包含多种实体类型的非规范化源表，并在整合过程中将这些表分解为独立的实体相关关系。为评估SINT-Flow，我们引入SINT-Bench基准，包含10个模式整合任务，共93张关系表，其中包含描述多种实体类型的表。我们使用GPT-5.2及开源模型Qwen-3.6-27B作为核心模型进行评估。结果表明，SINT-Flow在实体类型检测上达到至少96%的F1分数，在属性检测上达到85%，在模式映射上达到83%。此外，通过消融实验验证了自一致性策略及审查循环在模式匹配算子中的有效性。
原文摘要 · Abstract (English)
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.
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