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    Título
    Profile generation system using artificial intelligence for information recovery and analysis
    Autor(es)
    Chamoso Santos, PabloAutoridad USAL ORCID
    Bartolomé, Álvaro
    García Retuerta, DavidAutoridad USAL ORCID
    Prieto Tejedor, JavierAutoridad USAL ORCID
    Prieta Pintado, Fernando de laAutoridad USAL ORCID
    Palabras clave
    Information recovery
    Information fusion
    Big data
    Profiling
    Fecha de publicación
    2020-04-15
    Editor
    Springer Nature
    Citación
    Chamoso, P., Bartolomé, Á., García-Retuerta, D. et al. Profile generation system using artificial intelligence for information recovery and analysis. J Ambient Intell Human Comput 11, 4583–4592 (2020). https://doi.org/10.1007/s12652-020-01942-y
    Resumen
    [EN]The advances in data computing and analysis methodologies have contributed to the added value of data. Several years ago it was di cult to imagine that we would ever be able to extract such a large amount of information from the Internet. All this thanks to the ability of current techniques to process large volumes of data in a short period of time. The Internet provides access to a large amount of unstructured or unlabelled data, which are hard to retrieve for any human due to the lack of knowledge of the available sources of information. Moreover, in many cases people are unaware of the online availability of their personal data. This article presents a system for retrieving personal information from the Internet on the basis of several input criteria. The system is capable of di erentiating the information of di erent people with the same name by using arti cial intelligence techniques. In the conducted case study, the information has been gathered from sources containing information about people living in Spain, but it could be adapted to the speci c sources of information of other countries. The system has been validated in a case study which included several participants and the obtained results have been quite satisfactory.
    URI
    https://hdl.handle.net/10366/163135
    ISSN
    1868-5137
    DOI
    10.1007/s12652-020-01942-y
    Versión del editor
    https://doi.org/10.1007/s12652-020-01942-y
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