Dealing with Matching Variability of Semantic Web Data Using Contexts

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Abstract

Goal of this paper is to propose a reference modeling framework to explicitly identify and formalize the different levels of variability that can arise along all the involved dimensions of a matching execution. The proposed framework is based on the notion of knowledge chunk, context, and mapping to abstract the variability levels and related operations along the source-dataset, the matching-dataset, and the mapping-set dimensions, respectively. An application of the proposed framework with instantiation in the HMatch 2.0 systems is illustrated.