Web实体的基准分块算法
Benchmarking Blocking Algorithms for Web Entities
摘要：
越来越多的实体是通过互联数据而不是网上文件来描述的。实体解析（Entity Resolution，ER）的目的是在数据网络中的一个或多个知识库中识别对同一真实世界实体的描述。为了减少描述之间所需的成对比较次数，ER方法通常执行称为\emph{blocking}的预处理步骤，该步骤将相似的实体描述放入块中，从而仅比较同一块中的描述。我们实验评估了几种阻塞方法提出的Web数据使用真实的数据集，其特点显着影响其有效性和效率。所提出的实验评估框架使我们能够更好地理解错过的匹配实体描述的特性，并将其与从不同类型的相关性链接中获得的地面真实进行对比。
英文摘要：
An increasing number of entities are described by interlinked data rather than documents on the Web. Entity Resolution (ER) aims to identify descriptions of the same real-world entity within one or across knowledge bases in the Web of data. To reduce the required number of pairwise comparisons among descriptions, ER methods typically perform a pre-processing step, called \emph{blocking}, which places similar entity descriptions into blocks and thus only compare descriptions within the same block. We experimentally evaluate several blocking methods proposed for the Web of data using real datasets, whose characteristics significantly impact their effectiveness and efficiency. The proposed experimental evaluation framework allows us to better understand the characteristics of the missed matching entity descriptions and contrast them with ground truth obtained from different kinds of relatedness links.
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。
