research-article

Leveraging conceptual lexicon: query disambiguation using proximity information for patent retrieval

Authors:

Parvaz Mahdabi,

Jimmy Xiangji Huang,

Fabio CrestaniAuthors Info & Claims

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

Pages 113 - 122

https://doi.org/10.1145/2484028.2484056

Published: 28 July 2013 Publication History

Abstract

Patent prior art search is a task in patent retrieval where the goal is to rank documents which describe prior art work related to a patent application. One of the main properties of patent retrieval is that the query topic is a full patent application and does not represent a focused information need. This query by document nature of patent retrieval introduces new challenges and requires new investigations specific to this problem. Researchers have addressed this problem by considering different information resources for query reduction and query disambiguation. However, previous work has not fully studied the effect of using proximity information and exploiting domain specific resources for performing query disambiguation.

In this paper, we first reduce the query document by taking the first claim of the document itself. We then build a query-specific patent lexicon based on definitions of the International Patent Classification (IPC). We study how to expand queries by selecting expansion terms from the lexicon that are focused on the query topic. The key problem is how to capture whether an expansion term is focused on the query topic or not. We address this problem by exploiting proximity information. We assign high weights to expansion terms appearing closer to query terms based on the intuition that terms closer to query terms are more likely to be related to the query topic.

Experimental results on two patent retrieval datasets show that the proposed method is effective and robust for query expansion, significantly outperforming the standard pseudo relevance feedback (PRF) and existing baselines in patent retrieval.

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Cited By

Albarede LMulhem PGoeuriot LMarié SLe Pape-Gardeux CChardin-Segui T(2023)Heterogeneous graph attention networks for passage retrievalInformation Retrieval10.1007/s10791-023-09424-326:1-2Online publication date: 16-Nov-2023
https://dl.acm.org/doi/10.1007/s10791-023-09424-3
Stamatis V(2022)End to End Neural Retrieval for Patent Prior Art SearchAdvances in Information Retrieval10.1007/978-3-030-99739-7_66(537-544)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99739-7_66
Albarede LMulhem PGoeuriot LLe Pape-Gardeux CMarie SChardin-Segui T(2022)Passage Retrieval on Structured Documents Using Graph Attention NetworksAdvances in Information Retrieval10.1007/978-3-030-99739-7_2(13-21)Online publication date: 10-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-030-99739-7_2
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Index Terms

Leveraging conceptual lexicon: query disambiguation using proximity information for patent retrieval
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking

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Published In

cover image ACM Conferences

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

July 2013

1188 pages

ISBN:9781450320344

DOI:10.1145/2484028

General Chairs:
Gareth J.F. Jones
Dublin City University, Ireland
,
Páraic Sheridan
Dublin City University, Ireland
,
Program Chairs:
Diane Kelly
University of North Carolina, Chapel Hill, USA
,
Maarten de Rijke
University of Amsterdam, The Netherlands
,
Tetsuya Sakai
Microsoft Research Asia, China

Copyright © 2013 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

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Published: 28 July 2013

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SIGIR '13

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SIGIR

SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval

July 28 - August 1, 2013

Dublin, Ireland

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SIGIR '13 Paper Acceptance Rate 73 of 366 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Albarede LMulhem PGoeuriot LMarié SLe Pape-Gardeux CChardin-Segui T(2023)Heterogeneous graph attention networks for passage retrievalInformation Retrieval10.1007/s10791-023-09424-326:1-2Online publication date: 16-Nov-2023
https://dl.acm.org/doi/10.1007/s10791-023-09424-3
Stamatis V(2022)End to End Neural Retrieval for Patent Prior Art SearchAdvances in Information Retrieval10.1007/978-3-030-99739-7_66(537-544)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99739-7_66
Albarede LMulhem PGoeuriot LLe Pape-Gardeux CMarie SChardin-Segui T(2022)Passage Retrieval on Structured Documents Using Graph Attention NetworksAdvances in Information Retrieval10.1007/978-3-030-99739-7_2(13-21)Online publication date: 10-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-030-99739-7_2
Vuong TAndolina SJacucci GRuotsalo T(2021)Does More Context Help? Effects of Context Window and Application Source on Retrieval PerformanceACM Transactions on Information Systems10.1145/347405540:2(1-40)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3474055
Wang FQian TLiu BPeng Z(2019)Patent expanded retrieval via word embedding under composite-domain perspectivesFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-018-7056-613:5(1048-1061)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1007/s11704-018-7056-6
Shalaby WZadrozny W(2019)Patent retrieval: a literature reviewKnowledge and Information Systems10.1007/s10115-018-1322-7Online publication date: 14-Jan-2019
https://doi.org/10.1007/s10115-018-1322-7
Song YHu WHe LDou L(2019)Enhancing the Healthcare Retrieval with a Self-adaptive Saturated Density FunctionAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-16148-4_39(501-513)Online publication date: 22-Mar-2019
https://doi.org/10.1007/978-3-030-16148-4_39
Shalaby WZadrozny WCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Toward an Interactive Patent Retrieval Framework based on Distributed RepresentationsThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210106(957-960)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210106
Zhang LLiu ZLi LShen CLi T(2018)PatSearchKnowledge and Information Systems10.1007/s10115-017-1127-057:1(135-158)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s10115-017-1127-0
Wang FLin L(2017)Exploiting semantic knowledge base for patent retrieval2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/FSKD.2017.8393111(2195-2200)Online publication date: Jul-2017
https://doi.org/10.1109/FSKD.2017.8393111
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