Reducing Uncertainty of Schema Matching via Crowdsourcing with Accuracy Rates
نویسندگان
چکیده
منابع مشابه
Reducing Uncertainty of Schema Matching via Crowdsourcing
Schema matching is a central challenge for data integration systems. Automated tools are often uncertain about schema matchings they suggest, and this uncertainty is inherent since it arises from the inability of the schema to fully capture the semantics of the represented data. Human common sense can often help. Inspired by the popularity and the success of easily accessible crowdsourcing plat...
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Schema matching is the process of establishing correspondences between the attributes of database schemas for data integration purposes. Although several automatic schema matching tools have been developed, their results are often incomplete or erroneous. To obtain a correct set of correspondences, usually human effort is required to validate the generated correspondences. This validation proce...
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In this paper, we propose to extend current practice in schema matching with the simultaneous use of top-K schema mappings rather than a single best mapping. This is a natural extension of existing methods (which can be considered to fall into the top-1 category), taking into account the imprecision inherent in the schema matching process. The essence of this method is the simultaneous generati...
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We address the problem of matching imperfectly documented schemas of data streams and large databases. Instancelevel schema matching algorithms identify likely correspondences between attributes by quantifying the similarity of their corresponding values. However, exact calculation of these similarities requires processing of all database records – which is infeasible for data streams. We devis...
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ژورنال
عنوان ژورنال: IEEE Transactions on Knowledge and Data Engineering
سال: 2020
ISSN: 1041-4347,1558-2191,2326-3865
DOI: 10.1109/tkde.2018.2881185