Semantic Similarity Calculation of TCM Patents in Intelligent Retrieval Based on Deep Learning

  • Na DengEmail author
  • Xu Chen
  • Caiquan Xiong
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 96)


Semantic similarity calculation between words is an important step of text analysis, mining and intelligent retrieval. It can help to achieve intelligent retrieval at the semantic level and improve the accuracy and recall rate of retrieval. Because of the particularity of TCM (Traditional Chinese Medicine) patents and the insufficiency of research, most of the current mainstream TCM patent retrieval systems are keywords-based, and the retrieval results are not satisfactory. In order to improve the intelligence level of TCM patent retrieval, to promote TCM innovation and avoid repetitive research, based on real TCM patent corpus, this paper utilizes the excellent feature learning ability of deep learning to build a neural network model, and gives a method to calculate the semantic similarity between words in TCM patents. The experimental results show that the proposed method is effective. In addition, this method can be extended to semantic similarity calculation in other domains.



This work was supported by National Key Research and Development Program of China under Grant 2017YFC1405403; National Natural Science Foundation of China under Grant 61075059; Philosophical and Social Sciences Research Project of Hubei Education Department under Grant 19Q054; Green Industry Technology Leading Project (product development category) of Hubei University of Technology under Grant CPYF2017008; Research Foundation for Advanced Talents of Hubei University of Technology under Grant BSQD12131; Natural Science Foundation of Anhui Province under Grant 1708085MF161; and Key Project of Natural Science Research of Universities in Anhui under Grant KJ2015A236.


  1. 1.
    Feng, L., Peng, Z.Y., Liu, B., et al.: A latent-citation-network based patent value evaluation method. J. Comput. Res. Dev. 52(3), 649–660 (2015)Google Scholar
  2. 2.
    Xu, N.Y.: A brief introduction to China’s major patent retrieval databases. China Invention and Patent, vol. 9, pp. 35–37. (2014)Google Scholar
  3. 3.
    Zhang, L., Liu, Z., Li, L., et al.: PatSearch: an integrated framework for patentability retrieval. Knowl. Inf. Syst. 57, 135–158 (2018)CrossRefGoogle Scholar
  4. 4.
    Shalaby, W., Zadrozny, W.: Toward an interactive patent retrieval framework based on distributed representations. In: The 40th ACM International SIGIR Conference on Research and Development in Information Retrieval. ACM, New York (2018)Google Scholar
  5. 5.
    Liu, B., Feng, L., Wang, F., et al.: Patent search and analysis supporting technology innovation. J. Commun. 37(3), 79–89 (2016)Google Scholar
  6. 6.
    Xu, K.: Research on query expansion of patent retrieval. Doctoral Dissertation of Dalian University of Technology (2017)Google Scholar
  7. 7.
    Yao, H.X.: Research on method of product innovative design based on patent knowledge. Master Dissertation of Zhejiang University (2016)Google Scholar
  8. 8.
    Zhao, Z., Yan, J., Fang, L., et al.: Measuring semantic similarity based on wordnet. In: Web Information Systems & Applications Conference. IEEE, Piscataway (2009)Google Scholar
  9. 9.
    Zhang, X., Sun, S., Zhang, K.: An information content-based approach for measuring concept semantic similarity in WordNet. Wirel. Pers. Commun. 3, 1–16 (2018)MathSciNetCrossRefGoogle Scholar
  10. 10.
    You, B., Liu, X.R., Ning, L., Yan, Y.S.: Using information content to evaluate semantic similarity on HowNet. In: The Eighth International Conference on Computational Intelligence & Security. IEEE, Piscataway (2013)Google Scholar
  11. 11.
    Dai, L., Liu, B., Xia, Y., et al.: Measuring semantic similarity between words using HowNet. In: International Conference on Computer Science & Information Technology. IEEE, Piscataway (2008)Google Scholar
  12. 12.
    Jiang, Y., Zhang, X., Tang, Y., et al.: Feature-based approaches to semantic similarity assessment of concepts using Wikipedia. Inf. Process. Manag. 51(3), 215–234 (2015)CrossRefGoogle Scholar
  13. 13.
    Shirakawa, M., Nakayama, K., Hara, T., et al.: Wikipedia-based semantic similarity measurements for noisy short texts using extended Naive Bayes. IEEE Trans. Emerg. Top. Comput. 3(2), 205–219 (2015)CrossRefGoogle Scholar
  14. 14.
    Mikolov, T., Chen, K., Corrado, G., et al.: Efficient estimation of word representations in vector space. Computer Science (2013)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer ScienceHubei University of TechnologyWuhanChina
  2. 2.School of Information and Safety EngineeringZhongnan University of Economics and LawWuhanChina

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