强大的组链接
Robust Group Linkage
摘要：
我们研究组链接的问题：链接记录，指的是同一组中的实体。组链接的应用包括在同一个链中查找企业，从同一个隶属关系中查找会议参与者，从同一个团队中查找球员等。首先，虽然同一组中的不同成员可以共享属性的一些相似的全局值，但它们代表不同的实体，因此对于相同或不同的属性也可以具有不同的局部值，这需要对值多样性的高容忍度。其次，组可能很大（有数万条记录），即使使用了良好的阻塞策略，也需要高的可伸缩性。我们提出了一个两阶段的算法：第一阶段确定的核心包含的记录，很可能属于同一组，而可能的错误值是强大的;第二阶段收集有力的证据，从核心，并利用它合并更多的记录到同一组，而宽容的属性的本地值的差异。实验结果表明，我们的算法在各种现实世界的数据集上的高效率和效率。
英文摘要：
We study the problem of group linkage: linking records that refer to entities in the same group. Applications for group linkage include finding businesses in the same chain, finding conference attendees from the same affiliation, finding players from the same team, etc. Group linkage faces challenges not present for traditional record linkage. First, although different members in the same group can share some similar global values of an attribute, they represent different entities so can also have distinct local values for the same or different attributes, requiring a high tolerance for value diversity. Second, groups can be huge (with tens of thousands of records), requiring high scalability even after using good blocking strategies. We present a two-stage algorithm: the first stage identifies cores containing records that are very likely to belong to the same group, while being robust to possible erroneous values; the second stage collects strong evidence from the cores and leverages it for merging more records into the same group, while being tolerant to differences in local values of an attribute. Experimental results show the high effectiveness and efficiency of our algorithm on various real-world data sets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
