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    Knowledge modeling in prior art search

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    Authors
    Graf, Erik
    Frommholz, Ingo
    Lalmas, Mounia
    Van Rijsbergen, Keith
    Issue Date
    2010
    Subjects
    information retrieval
    knowledge modelling
    
    Metadata
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    Abstract
    This study explores the benefits of integrating knowledge representations in prior art patent retrieval. Key to the introduced approach is the utilization of human judgment available in the form of classifications assigned to patent documents. The paper first outlines in detail how a methodology for the extraction of knowledge from such an hierarchical classification system can be established. Further potential ways of integrating this knowledge with existing Information Retrieval paradigms in a scalable and flexible manner are investigated. Finally based on these integration strategies the effectiveness in terms of recall and precision is evaluated in the context of a prior art search task for European patents. As a result of this evaluation it can be established that in general the proposed knowledge expansion techniques are particularly beneficial to recall and, with respect to optimizing field retrieval settings, further result in significant precision gains.
    Citation
    Graf, E., Frommholz, I., Lalmas, M. van Rijsbergen, K. (2010) 'Knowledge Modeling in Prior Art Search,' in Proceedings of the First Information Retrieval Facility Conference, IRFC 2010, vol. 6107: 31–46
    Publisher
    Springer
    URI
    http://hdl.handle.net/10547/275677
    DOI
    10.1007/978-3-642-13084-7_4
    Additional Links
    http://link.springer.com/chapter/10.1007%2F978-3-642-13084-7_4
    Type
    Book chapter
    Language
    en
    ISBN
    9783642130830
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-642-13084-7_4
    Scopus Count
    Collections
    Centre for Research in Distributed Technologies (CREDIT)

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