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    Ontology driven personal health knowledge discovery

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    Authors
    Yu, Hong Qing
    Zhao, Xia
    Deng, Zhikun
    Dong, Feng
    Affiliation
    University of Bedfordshire
    Issue Date
    2015-08-04
    Subjects
    healthcare
    semantic web and data mining
    knowledge discovery
    IoT
    Internet of Things
    
    Metadata
    Show full item record
    Other Titles
    Knowledge Management in Organizations. KMO 2015
    Abstract
    With fast development of smart sensor devices and mobile applications, all different kinds of information related to humans can be founded on the Internet that can be seen as a universal data repository or called Web of Data. Health or healthcare related data are not exceptional in the Web of Data age. The most important and valuable data comes from IoT such as sensors and mobile activity tracking applications to support developing self-health risk detection and management applications. This paper presents a comprehensive ontology driven knowledge discovery framework in personal health domain, which aims to reason and discover health knowledge from various data sources of IoT. The framework contains a sensor oriented Personal Wellness Knowledge Ontology and data integration architecture to complete a whole lifecycle of health knowledge detecting and reasoning path. In addition, a cloud computing based parallel semantic lifting algorithm is described for illustrating the semantic data generation process in detail.
    Citation
    Yu H.Q., Zhao X., Deng Z., Dong F. (2015) Ontology Driven Personal Health Knowledge Discovery. In: Uden L., Heričko M., Ting IH. (eds) Knowledge Management in Organizations. KMO 2015. Lecture Notes in Business Information Processing, vol 224. Springer, Cham.
    Publisher
    Springer Verlag
    URI
    http://hdl.handle.net/10547/624597
    DOI
    10.1007/978-3-319-21009-4_48
    Additional Links
    https://link.springer.com/chapter/10.1007/978-3-319-21009-4_48
    Type
    Book chapter
    Language
    en
    ISBN
    9783319210087
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-21009-4_48
    Scopus Count
    Collections
    Computing

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