Multilayer Collection Selection and Search of Topically Organized Patents
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Multilayer Collection Selection and Search of Topically Organized Patents

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We present a federated patent search system that explores three issues: (a) topical organization of patents based on their IPC, (b) collection selection of topically organised patent collections and ...

We present a federated patent search system that explores three issues: (a) topical organization of patents based on their IPC, (b) collection selection of topically organised patent collections and (c) integration of collection selection tools to patent search systems.

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    Multilayer Collection Selection and Search of Topically Organized Patents Multilayer Collection Selection and Search of Topically Organized Patents Presentation Transcript

    • Multilayer Collection Selection and Search of Topically Organized Patents Michail Salampasis Vienna University of Technology Anastasia Giahanou University of Macedonia Giorgos Paltoglou University of Wolverhampton
    • 2 Contents Overview: Aim and Objectives of this work Distributed Information Retrieval / Federated Search  Topically Organised Patents  Integration of DIR in patent search: Multilayer Source Selection  Experiment Setup  Results  Conclusions
    • Aim of this work 3 To explore the thematic organization of patent documents using the subdivision of patent data by International Patent Classification (IPC) codes , and if this organization can be used to build search tools that could improve patent search effectiveness using DIR methods
    • Which search tools and how should be integrated? 4 It is a mistake if we think the search tools which should be integrated into patent search systems depend only on existing IR or text processing technologies, Probably it has more to do with the attitude that a patent search is conducted. Furthermore, it is also very important to deeply understand a search process and how a specific tool can attain a specific objective of this process and therefore increase its efficiency.
    • If these parameters are not carefully considered 5 • Professional searchers will be skeptical and with a very conservative attitude towards adopting search methods, tools and technologies beyond the ones which dominated their domain. • A typical example is patent search where professional search experts typically use the Boolean search syntax and quite complex intellectual classification schemes
    • Understanding Patent Search processes * * Taken from Mihai Lupu and Allan Hanbury, Review Patent Retrieval
    • Objectives 7 •The improvement of our method relates to the very fundamental step in professional patent search (step 3 in the use case presented by Lupu and Hanbury) which is “defining a text query, potentially by Boolean operators and specific field filters”. • In prior art search probably the most important filter is based on the IPC (CPC now) classification
    • Objectives 8 •The method and tool which we present in this paper can support this step by automatically selecting IPCs given a query, make a filtered search based on the query and the automatically selected IPCs •The tool can be used for classification search which will be used as a starting point to identify and closer examine technical concepts as these are expressed in IPCs and to which a patent could be related
    • 9 Distributed IR Elements composing a Distributed Information Retrieval System . . . (1) Source Representation . . . .Collection 1 Collection 2 Collection 3 Collection 4 Collection Ν (2) Source Selection ………… (3) Results Merging User
    • Topically Organised Patents based on IPC taxonomy 10 IPC is a standard taxonomy for classifying patents, and has currently about 71,000 nodes which are organized into a five-level hierarchical system which is also extended in greater levels of granularity. Patent documents produced worldwide have manually-assigned classification codes which in our experiments are used to topically organize, distribute and index patents through hundreds or thousands of sub-collections.
    • Topically Organised Patents 11
    • Topically Organised Patents 12 The patents in average have three IPC codes. In the experiments we report here, we allocated a patent to each sub-collection specified by at least one of its IPC code, i.e. a sub-collection might overlap with others in terms of the patents it contains. IPC are assigned by humans in a very detailed and purposeful assignment process, something which is very different by the creation of sub-collections using automated clustering algorithms or the naive division method by chronological or source order, a division method which has been extensively used in past DIR research
    • Topically Organised Patents 13
    • Analysis of IPC distribution of topics and their relevant documents 14 IPC Level # of topics # relevant docs per topic (a) # of IPC classes of each topic (b) # of IPC classes of relevant docs (c) # of common IPC classes between (b) and (c) Training Split 3 300 8.22 2.08 4.8 1.76 Split 4 300 8.22 3.1 8.76 2.34 Split 5 300 8.22 5.82 19.84 3.63 Testing Split 3 300 8.57 2.09 5.15 1.75 Split 4 300 8.57 2.95 9.02 2.21 Split 5 300 8.57 5.58 20.56 3.73
    • Experiment Setup 15 We indexed the collection with the Lemur toolkit. The fields which have been indexed are: title, abstract, description (first 500 words), claims, inventor, applicant and IPC class information. Patent documents have been pre-processed to produce a single (virtual) document representing a patent. Our pre-processing involves also stop-word removal and stemming using the Porter stemmer. In the experiments reported here we use the Inquery algorithm implementation of Lemur
    • Two different types of Source Selection Algorithms were used 16 Hyper-document approach (CORI) o The main characteristic of CORI which is probably the most widely used and tested source selection method is that it creates a hyper-document representing all the documents-members of a sub- collection. Source Selection as Voting o This is a shift of focus from estimating the relevancy of each remote collection to explicitly estimating the number of relevant documents in each.
    • Source Selection Results (level 3) 17
    • Source Selection Results (level 4) 18
    • Source Selection Results (level 5) 19
    • Discussion • The superiority of CORI as source selection method is unquestionable • best runs are those requesting fewer sub-collections 10 or 20 and more documents from each selected sub- collection • This fact is probably the result of the small number of relevant documents which exist for each topic 20
    • Results of Retrieval Results SPLIT4 10 Collections Selected 20 Collections Selected Pres@100 MAP@100 Pres@100 MAP@100 Optimal 0.313 0.128 0.313 0.128 Centralised 0.257 0.105 0.257 0.105 CORI-CORI 0.203 0.081 0.213 0.086 CORI-SSL 0.221 0.091 0.231 0.097 BordaFuse-SSL 0.077 0.035 0.087 0.039 Multilayer 0.256 0.105 0.261 0.105 SPLIT5 10 Collections Selected 20 Collections Selected Pres@100 MAP@100 Pres@100 MAP@100 Optimal 0.346 0.146 0.351 0.148 Centralised 0.257 0.105 0.257 0.105 CORI-CORI 0.267 0.107 0.259 0.105 CORI-SSL 0.27 0.11 0.263 0.107 BordaFuse-SSL 0.03 0.02 0.04 0.028 Multilayer 0.269 0.106 0.267 0.102
    • Conclusions DIR approaches managed to perform better than the centralized index approaches, with 9 DIR combinations scoring better than the best centralized approach. Much more work is required: o We plan to explore further this line of work with exploring modifications to state-of-the-art DIR methods which didn’t perform well enough in this set of experiments o Also, we would like to experiment with larger distribution levels based on IPC (subgroup level). We plan to report the runs using split-5 in a future paper. 22
    • 23 Thank you…