多部实体分解的多维指派问题
Multidimensional Assignment Problem for multipartite entity resolution
摘要：
多部分实体解析的目的是将多个数据集的记录集成到一个实体中。我们推导出一个数学公式的一般类的记录链接问题的多部实体决议在许多数据集作为一个组合优化问题称为多维分配问题。作为我们的方法的动机，我们说明了顺序二分匹配的多部实体分辨率的优势。由于优化问题是NP难的，我们采用两个启发式程序，一个贪心算法和非常大规模的邻域搜索，解决分配问题，并找到最可能匹配的记录从多个数据集到一个单一的实体。我们评估和比较这些算法的性能和它们的修改合成生成的数据。我们进行计算实验比较性能最近的启发式，非常大规模的邻域搜索，与贪心算法，另一种启发式的MAP，以及与两个版本的遗传算法，一般的元启发式。重要的是，我们进行实验，以比较两种替代方法重新开始搜索前启发式，特别是随机抽样多启动和确定性设计为基础的多启动。我们发现的证据表明，基于设计的多启动可以更有效的数据库的规模越来越大。此外，我们表明，非常大规模的搜索，特别是它的多开始版本，优于简单的贪婪启发式。贪婪搜索与超大规模邻域搜索的混合提高了性能。使用多开始与少至三个额外的运行非常大规模的搜索提供了一些改进的性能非常大规模的搜索过程。最后，我们提出了一种方法来评估的非常大规模的邻域搜索的复杂性。
英文摘要：
Multipartite entity resolution aims at integrating records from multiple datasets into one entity. We derive a mathematical formulation for a general class of record linkage problems in multipartite entity resolution across many datasets as a combinatorial optimization problem known as the multidimensional assignment problem. As a motivation for our approach, we illustrate the advantage of multipartite entity resolution over sequential bipartite matching. Because the optimization problem is NP-hard, we apply two heuristic procedures, a Greedy algorithm and very large scale neighborhood search, to solve the assignment problem and find the most likely matching of records from multiple datasets into a single entity. We evaluate and compare the performance of these algorithms and their modifications on synthetically generated data. We perform computational experiments to compare performance of recent heuristic, the very large-scale neighborhood search, with a Greedy algorithm, another heuristic for the MAP, as well as with two versions of genetic algorithm, a general metaheuristic. Importantly, we perform experiments to compare two alternative methods of re-starting the search for the former heuristic, specifically a random-sampling multi-start and a deterministic design-based multi-start. We find evidence that design-based multi-start can be more efficient as the size of databases grow large. In addition, we show that very large scale search, especially its multi-start version, outperforms simple Greedy heuristic. Hybridization of Greedy search with very large scale neighborhood search improves the performance. Using multi-start with as few as three additional runs of very large scale search offers some improvement in the performance of the very large scale search procedure. Last, we propose an approach to evaluating complexity of the very large-scale neighborhood search.
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