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Ontology Matching is a process to find correspondences between semantically related entities of two ontologies. Most matching systems do evaluation by comparing the correspondences with reference alignment. Since 2010 another method has been used to measure a logic-based of correspondence or mapping, called incoherent mapping measurement. The more incoherent of the mapping the lower quality of mapping...
In many applications, such as data integration and big data analytics, one has to integrate data from multiple sources without detailed and accurate schema information. The state of the art focuses on matching attributes among sources based on the information derived from the data in those sources. However, a best join result according to a method's own pre-determined criteria may not fit a user's...
Pairwise link discovery approaches for the Web of Data do not scale to many sources thereby limiting the potential for data integration. We thus propose a holistic approach for linking many data sources based on a clustering of entities representing the same real-world object. Our clustering approach utilizes existing links and can deal with entities of different semantic types. The approach is able...
Integration of data is performed to provide user a unified view of heterogeneous data sources residing on different places. Major challenge in data integration process is; first identify and then resolve the data level conflicts. Ignoring identification and resolution of these conflicts produces inconsistent and erroneous data. In this paper we identify and resolve a new data level conflict. Data...
The increasing size and availability of web data make data quality a core challenge in many applications. Principles of data quality are recognized as essential to ensure that data fit for their intended use in operations, decision-making, and planning. However, with the rise of the Semantic Web, new data quality issues appear and require deeper consideration. In this paper, we propose to extend the...
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