Nguyen Thanh Tam

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As the number of publicly-available datasets are likely to grow, the demand of establishing the links between these datasets is also getting higher and higher. For creating such links we need to match their schemas. Moreover, for using these datasets in meaningful ways, one often needs to match not only two, but several schemas. This matching process(More)
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, a human expert is usually required to validate the(More)
As the volumes of AI problems involving human knowledge are likely to soar, crowdsourcing has become essential in a wide range of world-wide-web applications. One of the biggest challenges of crowdsourcing is aggregating the answers collected from crowd workers; and thus, many aggregate techniques have been proposed. However, given a new application, it is(More)
As the number of schema repositories grows rapidly and several web-based platforms exist to support publishing schemas, schema reuse becomes a new trend. Schema reuse is a methodology that allows users to create new schemas by copying and adapting existing ones. This methodology supports to reduce not only the effort of designing new schemas but also the(More)
The amount of information available on the Web has been growing dramatically, raising the importance of techniques for searching the Web. Recently, Web Tables emerged as a model, which enables users to search for information in a structured way. However, effective presentation of results for Web Table search requires (1) selecting a ranking of tables that(More)
Schema matching supports data integration by establishing correspondences between the attributes of independently designed database schemas. In recent years, various tools for automatic pair-wise matching of schemas have been developed. Since the matching process is inherently uncertain, the correspondences generated by such tools are often validated by a(More)
As the volumes of AI problems involving human knowledge are likely to soar, crowdsourcing has become essential in a wide range of worldwide web applications. One of the biggest challenges of crowdsourcing is aggregating the answers collected from the crowd since the workers might have wide-ranging levels of expertise. In order to tackle this challenge, many(More)
As the number of scientific papers getting published is likely to soar, most of modern paper management systems (e.g. ScienceWise, Mendeley, CiteU-Like) support tag-based retrieval. In that, each paper is associated with a set of tags, allowing user to search for relevant papers by formulating tag-based queries against the system. One of the most critical(More)