SERIMI -资源描述相似性、RDF实例匹配和互连
SERIMI - Resource Description Similarity, RDF Instance Matching and Interlinking
摘要：
在关联数据云中发布的数据集的互连是一个具有挑战性的问题，也是语义Web成功的关键因素。基于规则的手动方法是解决这个问题的最有效的方法，但它们需要熟练的人类数据发布者经历一个费力、容易出错和耗时的过程，以手动描述两个数据集之间的规则映射实例。因此，解决这个问题的自动方法是非常受欢迎的。在本文中，我们提出了一种新的互连方法，SERIMI，自动解决这个问题。SERIMI匹配源数据集和目标数据集之间的实例，而无需事先了解这些数据集的数据、域或模式。与基准集合进行的实验表明，我们的方法大大优于国家的最先进的自动方法来解决关联数据云上的互连问题。
英文摘要：
The interlinking of datasets published in the Linked Data Cloud is a challenging problem and a key factor for the success of the Semantic Web. Manual rule-based methods are the most effective solution for the problem, but they require skilled human data publishers going through a laborious, error prone and time-consuming process for manually describing rules mapping instances between two datasets. Thus, an automatic approach for solving this problem is more than welcome. In this paper, we propose a novel interlinking method, SERIMI, for solving this problem automatically. SERIMI matches instances between a source and a target datasets, without prior knowledge of the data, domain or schema of these datasets. Experiments conducted with benchmark collections demonstrate that our approach considerably outperforms state-of-the-art automatic approaches for solving the interlinking problem on the Linked Data Cloud.
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。
