Unsupervised Blocking Key Selection for Real-Time Entity Resolution
Unsupervised Blocking Key Selection for Real-Time Entity Resolution
复制标题
用于实时实体解析的无监督阻塞键选择
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
P. Christen
中科院分区：
文献类型：
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作者：
Banda Ramadan;P. Christen
实时实体解析 (ER) 是在亚秒级时间内将查询记录与数据库中表示同一现实世界实体的记录进行匹配的过程。索引是 ER 过程中的一个主要步骤，旨在通过使用阻塞键标准使相似记录彼此更接近来减少搜索空间。选择这些键对于实时 ER 流程的有效性和效率至关重要。传统的索引技术需要领域知识来进行最佳的键选择。然而，为了使 ER 过程减少对人类领域知识的依赖，需要自动选择最佳阻塞密钥。在本文中，我们提出了一种无监督学习技术，该技术可以自动选择最佳的阻塞键来构建可在实时 ER 中使用的索引。我们专门学习了与多遍排序邻域一起使用的多个键，这是 ER 中最有效且最广泛使用的索引技术之一。我们使用三个真实数据集评估所提出的方法，并将其与现有的自动阻塞密钥选择技术进行比较。结果表明，我们的方法学习了适合实时 ER 的最佳阻塞/排序键。学习到的键显着提高了查询匹配的效率，同时保持了匹配结果的质量。
Real-time entity resolution (ER) is the process of matching query records in sub-second time with records in a database that represent the same real-world entity. Indexing is a major step in the ER process, aimed at reducing the search space by bringing similar records closer to each other using a blocking key criterion. Selecting these keys is crucial for the effectiveness and efficiency of the real-time ER process. Traditional indexing techniques require domain knowledge for optimal key selection. However, to make the ER process less dependent on human domain knowledge, automatic selection of optimal blocking keys is required. In this paper we propose an unsupervised learning technique that automatically selects optimal blocking keys for building indexes that can be used in real-time ER. We specifically learn multiple keys to be used with multi-pass sorted neighbourhood, one of the most efficient and widely used indexing techniques for ER. We evaluate the proposed approach using three real-world data sets, and compare it with an existing automatic blocking key selection technique. The results show that our approach learns optimal blocking/sorting keys that are suitable for real-time ER. The learnt keys significantly increase the efficiency of query matching while maintaining the quality of matching results.