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arXiv · 2403.06434

BoostER: Leveraging Large Language Models for Enhancing Entity Resolution

Huahang Li·Shuangyin Li·Fei Hao·Chen Jason 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.

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Huahang Li, Shuangyin Li, Fei Hao, Chen Jason Zhang, Yuanfeng Song, Lei Chen. 2024-03-11. BoostER: Leveraging Large Language Models for Enhancing Entity Resolution. https://doi.org/10.1145/3589335.3651245%2010.1145%2F3589335.3651245%2010.1145%2F3589335.3651245

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