Abstract
Prior art search is one of the most common forms of patent search, whose goal is to find patent documents that constitute prior art for a given patent being examined. Current patent search systems are mostly keyword-based, and due to the unique characteristics of patents and their usage, such as embedded structure and the length of patent documents, there are rooms for further improvements. In this paper, we propose a new query formulation method by using keyword dependency relations and semantic tags, which have not been used for prior art search. The key idea of this paper is to make use of patent structure, linguistic clues and use word relations to identify important terms. Moreover, to formulate better queries we attempt to identify what technology area a patent belongs to and what problems/solutions it addresses. Based on our experiments where IPC codes are used for relevance judgments, we show that keyword dependency relation approach achieved 13~18% improvement in MAP over the traditional tf-idf based term weighting method when a single field is used for query formulation. Furthermore, we obtain 42~46% improvement in MAP when additional terms are used through pattern-based semantic tagging.
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References
Iwayama, M., Fujii, A., Kando, N., Takano, A.: Overview of patent retrieval task at NTCIR-3. In: Proceedings of NTCIR Workshop (2009)
Fujii, A., Iwayama, M., Kando, N.: Overview of Patent Retrieval Task at NTCIR- 4. In: Proceedings of NTCIR-4 Workshop (2004)
Kim, Y., et al.: Automatic Discovery of Technology Trends from Patent. In: Proceedings of the 2009 ACM Symposium on Applied Computing, pp. 1480–1487 (2009)
Kazuya, K.: Query Term Extraction from patent documents for invalidity search. In: Proceedings of NTCIR-5 Workshop Meeting, Tokyo, Japan, December 6-9 (2005)
Roda, G., Tait, J., Piroi, F., Zenz, V.: CLEF-IP 2009: Retrieval experiments in the Intellectual Property domain. In: CLEF-IP (2009)
Susan, V., Eva, D.: Prior Art retrieval using the claims section as a bag of words. In: CLEF-IP (2010)
Toucedo, J.C., Losada, D.E.: University of Santiago de Compostela at CLEF-IP09. In: 1st CLEF-IP, Corfu, Greece (2009)
Xiaobing, X., Bruce Croft, W.: Transforming Patents into Prior Art Queries. In: SIGIR 2009 (2010)
Metti, Z., et al.: Prior art retrieval using various patent document fields contents. In: CLEF-IP (2010)
Mai, F.-D., Hwang, F., Chien, K.-M., Wang, Y.-M., Chen, C.-Y.: Patent map and analysis of carbon nanotube. Science and Technology Information Center, National Science Council, ROC (2002)
Young Gil, K., et al.: Visualization of patent analysis for emerging technology. Expert Systems with Applications: An International Journal archive 34(3) (April 2008)
Lent, B., et al.: Discovering trends in text databases. In: Proc. 3rd Int. Conf. Knowledge Discovery and Data Mining, KDD, pp. 227–230 (1997)
The Lemur Toolkit, http://www.lemurproject.org
Takaki, et al.: Associative Document Retrieval by Query Subtopic Analysis and its Application to Invalidity Patent Search. In: Proceedings of CIKM (2004)
Zheng, W., Zhang, Y., Hong, Y., Fan, J., Liu, T.: Topic Tracking Based on Keywords Dependency Profile. In: Li, H., Liu, T., Ma, W.-Y., Sakai, T., Wong, K.-F., Zhou, G. (eds.) AIRS 2008. LNCS, vol. 4993, pp. 129–140. Springer, Heidelberg (2008)
The USPTO databased, http://www.uspto.gov/
Kim, J.-H., et al.: Patent document categorization based on semantic structural information. Information Processing and Management (2007)
Xue, X., Bruce Croft, W.: Automatic Query Generation for Patent Search. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009 (2009)
Lupu, M., Mayer, K., Tait, J., Trippe, A.J.: Current Challenges in Patent Information Retrieval. The Information Retrieval Series 29 (2011)
Hunt, D., Nguyen, L., Rodgers, M.: Patent searching: tools & techniques (2007)
trect_eval program at TRECT website, trec.nist.gov/trec_eval
Open NLP POStagger, http://opennlp.sourceforge.net/
van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworths, London (1979), http://www.dcs.gla.ac.uk/Keith/Preface.html
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Nguyen, KL., Myaeng, SH. (2012). Query Enhancement for Patent Prior-Art-Search Based on Keyterm Dependency Relations and Semantic Tags. In: Salampasis, M., Larsen, B. (eds) Multidisciplinary Information Retrieval. IRFC 2012. Lecture Notes in Computer Science, vol 7356. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31274-8_3
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DOI: https://doi.org/10.1007/978-3-642-31274-8_3
Publisher Name: Springer, Berlin, Heidelberg
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