用大模型解决多表实体匹配中的语义不一致和效率问题
Unlocking the Power of Large Language Models for Multi-table Entity Matching
- 设计多风格提示增强模块，统一不同表格的数值属性语义
- 通过传递性共识嵌入提升匹配效率，处理多源数据膨胀问题
- 引入密度感知剪枝，过滤噪声实体，提升匹配质量
多表实体匹配（MEM）突破了双表方法的局限，可在无唯一标识的情况下同时识别多个数据源中的等价实体。然而，现有基于预训练语言模型的方法难以应对因数值属性差异引发的语义不一致问题。受大语言模型（LLM）强大语言理解能力启发，本文提出一种新型LLM驱动的多表实体匹配框架——LLM4MEM。首先，设计多风格提示增强的属性协调模块以解决语义不一致；其次，为缓解多源数据导致的实体数量激增带来的匹配效率瓶颈，提出传递性共识嵌入匹配模块，优化嵌入与预匹配过程；最后，针对匹配过程中的噪声实体问题，引入密度感知剪枝模块以提升匹配质量。在6个MEM数据集上的实验表明，相比基线模型，本方法平均F1提升5.1%。代码已开源：https://github.com/Ymeki/LLM4MEM。
原文摘要 · Abstract (English)
Multi-table entity matching (MEM) addresses the limitations of dual-table approaches by enabling simultaneous identification of equivalent entities across multiple data sources without unique identifiers. However, existing methods relying on pre-trained language models struggle to handle semantic inconsistencies caused by numerical attribute variations. Inspired by the powerful language understanding capabilities of large language models (LLMs), we propose a novel LLM-based framework for multi-table entity matching, termed LLM4MEM. Specifically, we first propose a multi-style prompt-enhanced LLM attribute coordination module to address semantic inconsistencies. Then, to alleviate the matching efficiency problem caused by the surge in the number of entities brought by multiple data sources, we develop a transitive consensus embedding matching module to tackle entity embedding and pre-matching issues. Finally, to address the issue of noisy entities during the matching process, we introduce a density-aware pruning module to optimize the quality of multi-table entity matching. We conducted extensive experiments on 6 MEM datasets, and the results show that our model improves by an average of 5.1% in F1 compared with the baseline model. Our code is available at https://github.com/Ymeki/LLM4MEM.
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