multilayer collection selection and search of topically organized patents

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

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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

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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?

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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

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• 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

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•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

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•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

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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

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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.


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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


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Analysis of IPC distribution of topics and their relevant documents

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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

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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

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Hyper-document approach (CORI)

oThe 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

oThis 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)

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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

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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:

oWe 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

oAlso, 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.

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Thank you…