Hostname: page-component-745bb68f8f-grxwn Total loading time: 0 Render date: 2025-01-14T04:37:51.717Z Has data issue: false hasContentIssue false
  • English
  • Français

Engineering Knowledge Graph for Keyword Discovery in Patent Search

Part of: Innovation Engineering

Published online by Cambridge University Press:  26 July 2019

Serhad Sarica ,
Binyang Song ,
En Low  and
Jianxi Luo
Serhad Sarica*
Affiliation:
Singapore University of Technology and Design, Engineering Product Development Pillar;
Binyang Song
Affiliation:
Singapore University of Technology and Design, Engineering Product Development Pillar;
En Low
Affiliation:
Singapore University of Technology and Design
Jianxi Luo
Affiliation:
Singapore University of Technology and Design, Engineering Product Development Pillar;
*
Contact: Sarica, Serhad, Singapore University of Technology and Design Engineering Product Development, Singapore, serhadsarica@gmail.com

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Patent retrieval and analytics have become common tasks in engineering design and innovation. Keyword-based search is the most common method and the core of integrative methods for patent retrieval. Often searchers intuitively choose keywords according to their knowledge on the search interest which may limit the coverage of the retrieval. Although one can identify additional keywords via reading patent texts from prior searches to refine the query terms heuristically, the process is tedious, time-consuming, and prone to human errors. In this paper, we propose a method to automate and augment the heuristic and iterative keyword discovery process. Specifically, we train a semantic engineering knowledge graph on the full patent database using natural language processing and semantic analysis, and use it as the basis to retrieve and rank the keywords contained in the retrieved patents. On this basis, searchers do not need to read patent texts but just select among the recommended keywords to expand their queries. The proposed method improves the completeness of the search keyword set and reduces the human effort for the same task.

Keywords

Semantic data processingOntologiesMachine learningEngineering knowledge graphKeyword discovery
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 2249 - 2258
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

References

Alberts, D., Yang, C. B., Fobare-DePonio, D., Koubek, K., Robins, S., Rodgers, M. and DeMarco, D. (2011), “Introduction to patent searching”, In Current challenges in patent information retrieval. Springer Berlin Heidelberg. https://doi/org/10.1007/978-3-642-19231-9_1Google Scholar
Altshuller, G. S. and Shapiro, R. B. (1956), “О Психологии изобретательского творчества (On the psychology of inventive creation)(in Russian).”, Вопросы Психологии (The Psychological Issues), Vol. 6 (Вопросы Психологии (The Psychol. Issues)), pp. 37–49.Google Scholar
Benson, C. L. and Magee, C. L. (2013), “A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field”, Scientometrics, Vol. 96 No. 1, pp. 69–82. https://doi/org/10.1007/s11192-012-0930-3Google Scholar
Bicchi, A., Balluchi, A., Prattichizzo, D. and Gorelli, A. (1997), “Introducing the “SPHERICLE”: an experimental testbed\nfor research and teaching in nonholonomy”, Proceedings of International Conference on Robotics and Automation. https://doi/org/10.1109/ROBOT.1997.619356Google Scholar
Cascini, G. and Russo, D. (2006), “Computer-Aided analysis of patents and search for TRIZ contradictions”, International Journal of Product Development, Vol. 4 No. 1–2, pp. 52–67. https://doi/org/10.1504/IJPD.2007.011533Google Scholar
D'hondt, E. (2009), “Lexical issues of a syntactic approach to interactive patent retrieval”, The Proceedings of the 3rd BCSIRSG Symposium on …, pp. 102–109. Available at: http://lands.let.kun.nl/literature/dhondt.2009.1.pdf.Google Scholar
Diaz, F., Mitra, B. and Craswell, N. (2016), “Query Expansion with Locally-Trained Word Embeddings”, In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), , Stroudsburg, PA, USA, pp. 367–377. https://doi/org/10.18653/v1/P16-1035Google Scholar
Fellbaum, C. (1998), “WordNet: An Electronic Lexical Database”. MIT Press, Cambridge, MA.Google Scholar
Fu, K., Cagan, J., Kotovsky, K. and Wood, K. (2013), “Discovering Structure in Design Databases Through Functional and Surface Based Mapping”, Journal of Mechanical Design, Vol. 135 (March 2013), p. 031006. https://doi/org/10.1115/1.4023484Google Scholar
Fu, K., Murphy, J., Yang, M., Otto, K., Jensen, D. and Wood, K. (2014), “Design-by-analogy: experimental evaluation of a functional analogy search methodology for concept generation improvement”, Research in Engineering Design, Vol. 26 No. 1, pp. 77–95. https://doi/org/10.1007/s00163-014-0186-4Google Scholar
Fujii, A. (2007), “Enhancing patent retrieval by citation analysis”, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR ’07, pp. 793–794. https://doi/org/10.1145/1277741.1277912Google Scholar
Gerken, J. M. and Moehrle, M. G. (2012), “A new instrument for technology monitoring: Novelty in patents measured by semantic patent analysis”, Scientometrics, Vol. 91 No. 3, pp. 645–670. https://doi/org/10.1007/s11192-012-0635-7Google Scholar
Graf, E., Frommholz, I., Lalmas, M. and Van Rijsbergen, K. (2010), “Knowledge modeling in prior art search”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6107 LNCS, pp. 31–46. https://doi/org/10.1007/978-3-642-13084-7_4Google Scholar
He, Y. and Luo, J. (2017), “The novelty “sweet spot” of invention”, Design Science. Cambridge University Press, Vol. 3, p. e21. https://doi/org/10.1017/dsj.2017.23Google Scholar
Jeong, Y., Lee, K., Yoon, B. and Phaal, R. (2015), “Development of a patent roadmap through the Generative Topographic Mapping and Bass diffusion model”, Journal of Engineering and Technology Management - JET-M. Elsevier B.V., Vol. 38, pp. 53–70. https://doi/org/10.1016/j.jengtecman.2015.08.006Google Scholar
Kim, G., Kwon, Y., Suh, E. S. and Ahn, J. (2016), “Analysis of Architectural Complexity for Product Family and Platform”, Journal of Mechanical Design, pp. 1–11. https://doi/org/10.1115/1.4033504Google Scholar
Koch, S., Bosch, H., Giereth, M. and Ertl, T. (2011), “Iterative integration of visual insights during scalable patent search and analysis”, IEEE Transactions on Visualization and Computer Graphics, Vol. 17 No. 5, pp. 557–569. https://doi/org/10.1109/TVCG.2010.85Google Scholar
Kuzi, S., Shtok, A. and Kurland, O. (2016), “Query Expansion Using Word Embeddings”, In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM ’16. , New York, New York, USA, pp. 1929–1932. https://doi/org/10.1145/2983323.2983876Google Scholar
Mikolov, T., Chen, K., Corrado, G. and Dean, J. (2013a), “Efficient Estimation of Word Representations in Vector Space”, Available at: http://arxiv.org/abs/1301.3781 (Accessed: 26 November 2018).Google Scholar
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S. and Dean, J. (2013b), “Distributed Representations of Words and Phrases and their Compositionality”, pp. 3111–3119. Available at: http://papers.nips.cc/paper/5021-distributed-representations-of-words-andphrases (Accessed: 26 November 2018).Google Scholar
Montecchi, T., Russo, D. and Liu, Y. (2013), “Searching in Cooperative Patent Classification: Comparison between keyword and concept-based search”, Advanced Engineering Informatics. Elsevier, Vol. 27 No. 3, pp. 335–345. https://doi/org/10.1016/J.AEI.2013.02.002Google Scholar
Mukherjea, S. (2005), “Information retrieval and knowledge discovery utilising a biomedical Semantic Web”, Briefings in Bioinformatics, Vol. 6 No. 3, pp. 252–262. https://doi/org/10.1093/bib/6.3.252Google Scholar
Murphy, J., Fu, K., Otto, K., Yang, M., Jensen, D. and Wood, K. (2014), “Function Based Design-by-Analogy: A Functional Vector Approach to Analogical Search”, Journal of Mechanical Design, Vol. 136 No. 10, p. 101102. https://doi/org/10.1115/1.4028093Google Scholar
Nakamura, H., Suzuki, S., Sakata, I. and Kajikawa, Y. (2015), “Knowledge combination modeling: The measurement of knowledge similarity between different technological domains”, Technological Forecasting and Social Change. Elsevier Inc., Vol. 94, pp. 187–201. https://doi/org/10.1016/j.techfore.2014.09.009Google Scholar
Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010), “Automatic Keyword Extraction from Individual Documents”, in Text Mining. John Wiley & Sons, Ltd Chichester, UK, pp. 1–20. https://doi/org/10.1002/9780470689646.ch1Google Scholar
Roy, D., Paul, D., Mitra, M. and Garain, U. (2016), “Using Word Embeddings for Automatic Query Expansion”, Available at: http://arxiv.org/abs/1606.07608 (Accessed: 4 December 2018).Google Scholar
Song, B. and Luo, J. (2017), “Mining Patent Precedents for Data-Driven Design : The Case of Spherical Rolling Robots”, Journal of Mechanical Design, Vol. 139 No. 11, p. 111420. https://doi/org/10.1115/1.4037613Google Scholar
Song, B., Luo, J. and Wood, K. L. (2018), “Data-Driven Platform Design: Patent Data and Function Network Analysis”, Journal of Mechanical Design, In Press.Google Scholar
Srinivasan, V., Song, B., Luo, J., Subburaj, K., Elara, M. R., Blessing, L. and Wood, K. (2018), “Does Analogical Distance Affect Performance of Ideation?”, Journal of Mechanical Design. American Society of Mechanical Engineers, Vol. 140 No. 7, p. 071101. https://doi/org/10.1115/1.4040165Google Scholar
Steven, B., Ewan, K. and Edward, L. (2009), “Natural Language Processing with Python”, O'Reilly Media Inc.Google Scholar
Takaki, T., Fujii, A. and Ishikawa, T. (2004), “Associative document retrieval by query subtopic analysis and its application to invalidity patent search’, ACM Conference on Information and Knowledge Management, pp. 399–405. https://doi/org/10.1145/1031171.1031251Google Scholar
Wang, S.-J. (2011), “The state of art patent search with an example of human vaccines”, Human Vaccines, Vol. 7 No. 2, pp. 265–268. https://doi/org/10.4161/hv.7.2.14004Google Scholar
Wu, F., Vibhute, A., Soh, G. S., Wood, K. L. and Foong, S. (2017), “A compact magnetic field-based obstacle detection and avoidance system for miniature spherical robots”, Sensors (Switzerland ), Vol. 17 No. 6. https://doi/org/10.3390/s17061231Google Scholar
Xue, X. and Croft, W. B. (2009), “Automatic query generation for patent search”, In Proceeding of the 18th ACM conference on Information and knowledge management - CIKM ’09. , New York, New York, USA, p. 2037. https://doi/org/10.1145/1645953.1646295Google Scholar