通过分批查询优化大规模实体消歧，控制成本同时提升召回率。
Entity Resolution via Batched Oracle Queries
- 设计分批查询策略，逐批处理记录并聚类同实体
- 在六大数据集上实现更高召回率，且可控制查询次数
- 理论证明最优批次选择为NP难，提出特定条件下的最优解
我们研究一个每次只能处理有限批次记录的查询接口，该接口将属于同一真实实体的记录聚类。面对远超单批次规模的数据集，且任意批次无法保证包含某实体全部记录的情况，如何有效调用该接口进行实体消歧成为关键问题。目标是实现按需付费模式：完全掌控查询成本（查询次数），同时在每一步达到尽可能高的召回率。本文形式化定义此问题为分批实体消歧，并证明最优批次选择为NP-hard。在实体大小满足自然假设条件下，给出最优解。在六个真实数据集上的实验表明，该方法显著优于现有最先进基线。
原文摘要 · Abstract (English)
We consider an oracle that processes a limited batch of records at a time and clusters those that refer to the same real-world entity. We study how to interrogate such an oracle to resolve entities in a dataset whose size is far larger than a single batch, and where no batch is guaranteed to contain all records of any given entity. We aim at a pay-as-you-go approach, to have full control over the costs (the number of oracle consults), while achieving the highest possible recall at every step. We formally cast this problem as batched entity resolution, prove that selecting optimal batches is NP-hard, and provide an optimal solution under a natural condition on entity sizes. Finally, we evaluate our approach on six datasets and show its superiority over state-of-the-art baselines.
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