BoostER: Leveraging Large Language Models for Enhancing Entity Resolution

Huahang Li, Shuangyin Li, Fei Hao, C. Zhang, Yuanfeng Song, Lei Chen
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Abstract

Entity resolution, which involves identifying and merging records that refer to the same real-world entity, is a crucial task in areas like Web data integration. This importance is underscored by the presence of numerous duplicated and multi-version data resources on the Web. However, achieving high-quality entity resolution typically demands significant effort. The advent of Large Language Models (LLMs) like GPT-4 has demonstrated advanced linguistic capabilities, which can be a new paradigm for this task. In this paper, we propose a demonstration system named BoostER that examines the possibility of leveraging LLMs in the entity resolution process, revealing advantages in both easy deployment and low cost. Our approach optimally selects a set of matching questions and poses them to LLMs for verification, then refines the distribution of entity resolution results with the response of LLMs. This offers promising prospects to achieve a high-quality entity resolution result for real-world applications, especially to individuals or small companies without the need for extensive model training or significant financial investment.
BoostER:利用大型语言模型增强实体解析能力
实体解析涉及识别和合并指向同一现实世界实体的记录,是网络数据集成等领域的一项重要任务。网络上存在大量重复和多版本的数据资源,这就凸显了这项任务的重要性。然而,实现高质量的实体解析通常需要付出巨大的努力。像 GPT-4 这样的大型语言模型(LLM)的出现展示了先进的语言能力,可以成为这项任务的新范例。在本文中,我们提出了一个名为 BoostER 的演示系统,该系统研究了在实体解析过程中利用 LLM 的可能性,揭示了 LLM 在易于部署和低成本方面的优势。我们的方法以最佳方式选择一组匹配问题,并将其提交给 LLMs 进行验证,然后根据 LLMs 的响应完善实体解析结果的分布。这为现实世界的应用,尤其是个人或小公司的应用,提供了实现高质量实体解析结果的广阔前景,而无需大量的模型训练或大量的资金投入。
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