Abstract
Patent retrieval has emerged as an important application of information retrieval (IR). It is considered to be a complex search task because patent search requires an extended chain of reasoning beyond basic document retrieval. As logic-based IR is capable of modelling both document retrieval and decision-making, it can be seen as a suitable framework for modelling patent data and search strategies. In particular, we demonstrate logic-based modelling for semantic data in patent documents and retrieval strategies which are tailored to patent search and exploit more than just the text in the documents. Given the expressiveness of logic-based IR, however, there is an attendant compromise on issues of scalability and quality. To address these trade-offs we suggest how a parallelised architecture can ensure that logical IR scales in spite of its expressiveness.
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Acknowledgements
We would like to thank Matrixware Information Services GmbH and the Information Retrieval Facility (IRF) for supporting this work. We also would like to thank Helmut Berger for his management of the LSLR project. Finally, many thanks to the reviewers for their excellent suggestions.
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Azzam, H., Klampanos, I.A., Roelleke, T. (2011). Large-Scale Logical Retrieval: Technology for Semantic Modelling of Patent Search. In: Lupu, M., Mayer, K., Tait, J., Trippe, A. (eds) Current Challenges in Patent Information Retrieval. The Information Retrieval Series, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19231-9_9
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