Abstract
Recently, instance matching has become a key technology to achieve interoperability over datasets, especially in linked data. Due the rapid growth of published datasets, it attracts increasingly more research interest. In this context, several approaches have been proposed. However, they do not perform well since the problem of matching instances that possess different descriptions is not addressed. On the other hand, the usage of the identity link owl:sameAs is generally predominant in linking correspondences. Unfortunately, many existing identity links are misused. In this paper, the authors discuss these issues and propose an original instance matching approach aiming to match instances that hold diverse descriptions. Furthermore, a novel link named ViewSameAs is proposed. The key improvement compared to existing approaches is alignment reuse. Thus, two novel methods are introduced: ViewSameAs-based clustering and alignment reuse based on metadata. Experiments on datasets by considering those of OAEI show that the proposed approach achieves satisfying and highly accuracy results.
Highlights
Data integration has been widely studied by the database community
To validate the effectiveness of the proposed approach, several tests are conducted on both OAEI Instance matching (IM) benchmarks and our datasets
The IM_VSA gets always the best results (Precision, Recall and F1-measure) than IM_PC which confirms the efficiency of the ViewSameAs-based clustering method in our proposal
Summary
Data integration has been widely studied by the database community. In the web of data and especially in linked data, it becomes one of the main issues in data sharing and exploitation. A significant number of existing owl:sameAs links on the Web of data do not adhere to their formal semantic (Halpin et al, 2010) This is due to the diverse contexts of instances descriptions. We propose a novel approach dealing with the IM problem where matching instances with diverse descriptions presents the principal aim. Some works combined existing basic similarity methods such as AIM-PC (Lu et al, 2018), ASL (Nguyen & Ichise, 2018) and SERIMI (Araujo et al, 2011; 2015), while others proposed novel formulas like FBEM (Stoermer & Rassadko, 2009), DSSim (Nagy et al, 2008) and HMatch (Castano et al, 2008) Like all these cited approaches, we are interested to the IM problem. Novel constructors are introduced: ViewSameAs for linking partially similar instances, hasBigInstance and hasBagClass which are served as metadata to refine the matching result
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