Entity resolution plays a significant role in enterprise systems where data integrity must be rigorously maintained. Traditional methods often struggle with handling noisy data or semantic understanding, while modern methods suffer from computational costs or the excessive need for parallel computation. In this study, we introduce a scalable hybrid framework, which is designed to address several important problems, including scalability, noise robustness, and reliable results. We utilized a pre-trained language model to encode each structured data into corresponding semantic embedding vectors. Subsequently, after retrieving a semantically relevant subset of candidates, we apply a syntactic verification stage using fuzzy string matching techniques to refine classification on the unlabeled data. This approach was applied to a real-world entity resolution task, which exposed a linkage between a central user management database and numerous shared hosting server records. Compared to other methods, this approach exhibits an outstanding performance in terms of both processing time and robustness, making it a reliable solution for a server-side product. Crucially, this efficiency does not compromise results, as the system maintains a high retrieval recall of approximately 0.97. The scalability of the framework makes it deployable on standard CPU-based infrastructure, offering a practical and effective solution for enterprise-level data integrity auditing.

翻译：实体解析在企业系统中扮演着重要角色，这些系统必须严格维护数据完整性。传统方法通常难以处理噪声数据或进行语义理解，而现代方法则受限于计算成本或对并行计算的过度需求。在本研究中，我们提出了一种可扩展的混合框架，旨在解决包括可扩展性、噪声鲁棒性和结果可靠性在内的若干重要问题。我们利用预训练语言模型将每条结构化数据编码为相应的语义嵌入向量。随后，在检索到语义相关的候选子集后，我们应用一个使用模糊字符串匹配技术的句法验证阶段，以优化对未标记数据的分类。该方法被应用于一个现实世界的实体解析任务，该任务揭示了中央用户管理数据库与众多共享托管服务器记录之间的关联。与其他方法相比，该方法在处理时间和鲁棒性方面均表现出色，使其成为服务器端产品的可靠解决方案。至关重要的是，这种效率并未以牺牲结果为代价，系统保持了约0.97的高检索召回率。该框架的可扩展性使其能够部署在标准的基于CPU的基础设施上，为企业级数据完整性审计提供了一个实用且有效的解决方案。