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.
- 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. Google ScholarDigital Library
- S. Bashir and A. Rauber. Improving retrievability of patents in prior-art search. In Proceedings of ECIR, pages 457--470, 2010. Google ScholarDigital Library
- S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Networks, 30(1-7):107--117, 1998. Google ScholarDigital Library
- E. D'hondt, S. Verberne, C. H. A. Koster, and L. Boves. Text representations for patent classification. Computational Linguistics, 39(3):755--775, 2013.Google ScholarCross Ref
- A. Fujii. Enhancing patent retrieval by citation analysis. In Proceedings of SIGIR, pages 793--794, 2007. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- J. M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604--632, 1999. Google ScholarDigital Library
- P. Lopez and L. Romary. Experiments with citation mining and key-term extraction for prior art search. CLEF (Notebook Papers/LABs/Workshops), 2010.Google Scholar
- M. Lupu and A. Hanbury. Patent Retrieval. Foundations and Trends in Information Retrieval, 2013. Google ScholarDigital Library
- 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.Google Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- D. M. Mimno and A. McCallum. Expertise modeling for matching papers with reviewers. In Proceedings of KDD, pages 500--509, 2007. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google Scholar
- A. Stolcke. SRILM - an extensible language modeling toolkit. In Proceedings of ICSLP, pages 901--904, 2002.Google Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- S. Wu, J. Sun, and J. Tang. Patent partner recommendation in enterprise social networks. In Proceedings of WSDM, pages 43--52, 2013. Google ScholarDigital Library
- J. Yang and J. Leskovec. Overlapping community detection at scale: a nonnegative matrix factorization approach. In Proceedings of WSDM, pages 587--596, 2013. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
Index Terms
- Query-Driven Mining of Citation Networks for Patent Citation Retrieval and Recommendation
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