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Query-Driven Mining of Citation Networks for Patent Citation Retrieval and Recommendation

Published: 03 November 2014 Publication History

Abstract

Prior art search or recommending citations for a patent application is a challenging task. Many approaches have been proposed and shown to be useful for prior art search. However, most of these methods do not consider the network structure for integrating and diffusion of different kinds of information present among tied patents in the citation network. In this paper, we propose a method based on a time-aware random walk on a weighted network of patent citations, the weights of which are characterized by contextual similarity relations between two nodes on the network. The goal of the random walker is to find influential documents in the citation network of a query patent, which can serve as candidates for drawing query terms and bigrams for query refinement. The experimental results on CLEF-IP datasets (CLEF-IP 2010 and CLEF-IP 2011) show the effectiveness of encoding contextual similarities (common classification codes, common inventor, and common applicant) between nodes in the citation network. Our proposed approach can achieve significantly better results in terms of recall and Mean Average Precision rates compared to strong baselines of prior art search.

References

[1]
R. A. Baeza-Yates, F. Saint-Jean, and C. Castillo. Web structure, dynamics and page quality. In Proceedings of String Processing and Information Retrieval (SPIRE), pages 117--130, 2002.
[2]
S. Bashir and A. Rauber. Improving retrievability of patents in prior-art search. In Proceedings of ECIR, pages 457--470, 2010.
[3]
S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Networks, 30(1-7):107--117, 1998.
[4]
E. D'hondt, S. Verberne, C. H. A. Koster, and L. Boves. Text representations for patent classification. Computational Linguistics, 39(3):755--775, 2013.
[5]
A. Fujii. Enhancing patent retrieval by citation analysis. In Proceedings of SIGIR, pages 793--794, 2007.
[6]
S. Katz. Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Transactions on Acoustics, Speech, and Signal Processing, 35(3):400--401, 1987.
[7]
J. M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604--632, 1999.
[8]
P. Lopez and L. Romary. Experiments with citation mining and key-term extraction for prior art search. CLEF (Notebook Papers/LABs/Workshops), 2010.
[9]
M. Lupu and A. Hanbury. Patent Retrieval. Foundations and Trends in Information Retrieval, 2013.
[10]
W. Magdy and G. J. F. Jones. Applying the KISS principle for the CLEF-IP 2010 prior art candidate patent search task. CLEF (Notebook Papers/LABs/Workshops), 2010.
[11]
W. Magdy and G. J. F. Jones. PRES: A score metric for evaluating recall-oriented information retrieval applications. In Proceedings of SIGIR, pages 611--618, 2010.
[12]
P. Mahdabi, L. Andersson, M. Keikha, and F. Crestani. Automatic refinement of patent queries using concept importance predictors. In Proceedings of SIGIR, pages 505--514, 2012.
[13]
P. Mahdabi, S. Gerani, J. X. Huang, and F. Crestani. Leveraging conceptual lexicon: Query disambiguation using proximity information for patent retrieval. In Proceedings of SIGIR, pages 113--122, 2013.
[14]
D. M. Mimno and A. McCallum. Expertise modeling for matching papers with reviewers. In Proceedings of KDD, pages 500--509, 2007.
[15]
S. Oh, Z. Lei, W.-C. Lee, P. Mitra, and J. Yen. CV-PCR: a context-guided value-driven framework for patent citation recommendation. In Proceedings of CIKM, pages 2291--2296, 2013.
[16]
F. Piroi, M. Lupu, A. Hanbury, and V. Zenz:. Clef-ip 2011: Retrieval in the intellectual property domain. In CLEF (Notebook Papers/Labs/Workshop), 2011.
[17]
A. Stolcke. SRILM - an extensible language modeling toolkit. In Proceedings of ICSLP, pages 901--904, 2002.
[18]
J. Tang, B. Wang, Y. Yang, P. Hu, Y. Zhao, X. Yan, B. Gao, M. Huang, P. Xu, W. Li, and A. K. Usadi. PatentMiner: topic-driven patent analysis and mining. In Proceedings of KDD, pages 1366--1374, 2012.
[19]
W. Tang, J. Tang, T. Lei, C. Tan, B. Gao, and T. Li. On optimization of expertise matching with various constraints. Neurocomputing, 76(1):71--83, 2012.
[20]
S. Wu, J. Sun, and J. Tang. Patent partner recommendation in enterprise social networks. In Proceedings of WSDM, pages 43--52, 2013.
[21]
J. Yang and J. Leskovec. Overlapping community detection at scale: a nonnegative matrix factorization approach. In Proceedings of WSDM, pages 587--596, 2013.
[22]
Y. Yang, J. Tang, J. Keomany, Y. Zhao, J. Li, Y. Ding, T. Li, and L. Wang. Mining competitive relationships by learning across heterogeneous networks. In Proceedings of CIKM, pages 1432--1441, 2012.
[23]
C. Zhai and J. D. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In Proceedings of SIGIR, pages 334--342, 2001.

Cited By

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  • (2024)Innovating Patent Retrieval: A Comprehensive Review of Techniques, Trends, and Challenges in Prior Art SearchesApplied System Innovation10.3390/asi70500917:5(91)Online publication date: 26-Sep-2024
  • (2024)Retrieval for Extremely Long Queries and Documents with RPRS: A Highly Efficient and Effective Transformer-based Re-RankerACM Transactions on Information Systems10.1145/363193842:5(1-32)Online publication date: 29-Apr-2024
  • (2024)ValidityValidity, Reliability, and Significance10.1007/978-3-031-57065-0_2(11-61)Online publication date: 10-Jun-2024
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  1. Query-Driven Mining of Citation Networks for Patent Citation Retrieval and Recommendation

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      cover image ACM Conferences
      CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
      November 2014
      2152 pages
      ISBN:9781450325981
      DOI:10.1145/2661829
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      Published: 03 November 2014

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      Author Tags

      1. bibliographic network
      2. citation graph
      3. prior art search

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      CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;
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      Cited By

      View all
      • (2024)Innovating Patent Retrieval: A Comprehensive Review of Techniques, Trends, and Challenges in Prior Art SearchesApplied System Innovation10.3390/asi70500917:5(91)Online publication date: 26-Sep-2024
      • (2024)Retrieval for Extremely Long Queries and Documents with RPRS: A Highly Efficient and Effective Transformer-based Re-RankerACM Transactions on Information Systems10.1145/363193842:5(1-32)Online publication date: 29-Apr-2024
      • (2024)ValidityValidity, Reliability, and Significance10.1007/978-3-031-57065-0_2(11-61)Online publication date: 10-Jun-2024
      • (2023)Interpretable patent recommendation with knowledge graph and deep learningScientific Reports10.1038/s41598-023-28766-y13:1Online publication date: 14-Feb-2023
      • (2022)A knowledge graph approach for recommending patents to companiesElectronic Commerce Research10.1007/s10660-021-09471-222:4(1435-1466)Online publication date: 1-Dec-2022
      • (2021)A personalized recommendation system for high-quality patent trading by leveraging hybrid patent analysisScientometrics10.1007/s11192-021-04180-x126:12(9369-9391)Online publication date: 1-Dec-2021
      • (2021)Chronological citation recommendation with time preferenceScientometrics10.1007/s11192-021-03878-2126:4(2991-3010)Online publication date: 1-Apr-2021
      • (2020)Semantic Based Heterogeneous Information Network Embedding for Patent Citation Recommendation2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE)10.1109/ICAICE51518.2020.00106(518-527)Online publication date: Oct-2020
      • (2020)A review of citation recommendation: from textual content to enriched contextScientometrics10.1007/s11192-019-03336-0122:3(1445-1472)Online publication date: 1-Mar-2020
      • (2019)Automating the search for a patent’s prior art with a full text similarity searchPLOS ONE10.1371/journal.pone.021210314:3(e0212103)Online publication date: 4-Mar-2019
      • Show More Cited By

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