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A Framework for Efficient Matching of Large-Scale Metadata Models
Title: | A Framework for Efficient Matching of Large-Scale Metadata Models |
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Authors: | Seham Moawed, Alsayed Algergawy, Amany Sarhan, and Ali Eldosouky |
Source: | Arabian Journal for Science and Engineering |
Place: | Springer (https://link.springer.com/journal/13369) |
Date: | 2018-07-01 |
Type: | Journal Paper |
Abstract: |
Despite the success achieved in the metadata models matching area, large-scale matching does not preserve high match quality and efficiency at the same time. To deal with these challenges, we introduce a generic matching framework, called MetMat, to identify and discover corresponding entities across XML schemas and/or ontologies (metadata models). In particular, the proposed framework is based on a parallelized clustering-based matching approach, which first splits the original matching task into smaller independent tasks. These independent tasks are then carried out in parallel exploiting desktop platform features that are equipped with parallelism enabled multi-core processors. To this end, we develop three different parallel strategies: inter-, intra-, and hybrid-matching strategies. To obtain high quality, a set of matchers are exploited. The proposed framework is validated through an extensive set of experiments over small and large datasets. We also compared the MetMat framework to top matching tools participating in the OAEI (Ontology Alignment Evaluation Initiative) |