集成多数据源的原理图匹配算法
Principled Graph Matching Algorithms for Integrating Multiple Data Sources
摘要：
本文探讨了多部图上的最大权图匹配问题的组合优化，这出现在集成多个数据源。实体解析（在结构化数据上执行噪声连接的数据集成问题）通常首先将每个记录散列成零个或多个块，对共同分块的记录对进行相似性评分，然后匹配足够相似的记录对。在最常见的匹配两个来源的情况下，通常希望最终匹配是一对一的（一个记录最多可以与另一个记录匹配）;数据库和统计记录链接社区的成员在最后阶段通过相似性得分的加权二分图匹配来完成这种匹配。这样的匹配直观上很有吸引力：它们利用了许多现实世界实体存储的自然全局属性-几乎被删除-并且已知可以显著提高精确度和召回率。不幸的是，与二部图的情况不同，多部图上的精确最大权重匹配是NP-困难的。我们的双重算法的贡献近似多方最大权重匹配：我们的第一个算法借用常见的贝叶斯概率推理的优化技术，我们的第二个是一个贪婪的近似算法。除了对后者的理论保证，我们提出了一个现实世界的ER问题，从Bing显着大于通常发现的文献，出版物的数据，并在一系列的合成问题的比较。我们的研究结果量化显着的改善，由于利用多个来源，这是可能的全球一对一的约束，否则独立的匹配子问题。我们还发现，我们的算法是互补的：一个是更强大的噪声下，另一个是简单的实现和非常快的运行。
英文摘要：
This paper explores combinatorial optimization for problems of max-weight graph matching on multi-partite graphs, which arise in integrating multiple data sources. Entity resolution-the data integration problem of performing noisy joins on structured data-typically proceeds by first hashing each record into zero or more blocks, scoring pairs of records that are co-blocked for similarity, and then matching pairs of sufficient similarity. In the most common case of matching two sources, it is often desirable for the final matching to be one-to-one (a record may be matched with at most one other); members of the database and statistical record linkage communities accomplish such matchings in the final stage by weighted bipartite graph matching on similarity scores. Such matchings are intuitively appealing: they leverage a natural global property of many real-world entity stores-that of being nearly deduped-and are known to provide significant improvements to precision and recall. Unfortunately unlike the bipartite case, exact max-weight matching on multi-partite graphs is known to be NP-hard. Our two-fold algorithmic contributions approximate multi-partite max-weight matching: our first algorithm borrows optimization techniques common to Bayesian probabilistic inference; our second is a greedy approximation algorithm. In addition to a theoretical guarantee on the latter, we present comparisons on a real-world ER problem from Bing significantly larger than typically found in the literature, publication data, and on a series of synthetic problems. Our results quantify significant improvements due to exploiting multiple sources, which are made possible by global one-to-one constraints linking otherwise independent matching sub-problems. We also discover that our algorithms are complementary: one being much more robust under noise, and the other being simple to implement and very fast to run.
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