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.
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Graf, E., Frommholz, I., Lalmas, M., van Rijsbergen, K. (2010). Knowledge Modeling in Prior Art Search. In: Cunningham, H., Hanbury, A., Rüger, S. (eds) Advances in Multidisciplinary Retrieval. IRFC 2010. Lecture Notes in Computer Science, vol 6107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13084-7_4
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