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An approach for detecting the commonality and specialty between scientific publications and patents

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Abstract

Scientific publications and patents are usually viewed as respective proxies of scientific research and technical development. There is considerable effort spent towards establishing topic linkages between science and technology with the lexical- or topic-based approaches. However, due to the heterogeneity between scholarly articles and patents in terms of purpose, statement, and quality, the performance is not satisfactory. To understand the difficulties of topic linkages and improve the performance, a framework is proposed to detect the commonality and specialty between scientific publications and patents from the two perspectives: linguistic characteristics and thematic structures. Extensive experimental results on the DrugBank dataset discover five commonness and five significant differences in terms of linguistic characteristics. For example, nouns are used most frequently among them, and scientific publications contain more word tokens than patent documents, but patents have usually longer sentences and use more clauses. In the meanwhile, common and special thematic structures are also uncovered between scientific publications and patents. The themes about general description in the pharmaceutical field are shared by two heterogeneous resources. The scientific publications tend to explain the disease mechanism and the medication content, while patents bias towards the preparation and practical application of drugs.

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Notes

  1. https://www.drugbank.ca/.

  2. https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.EFetch.

  3. http://ops.epo.org/.

  4. https://stanfordnlp.github.io/CoreNLP/index.html.

  5. https://nlp.stanford.edu/software/tregex.shtml.

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Acknowledgements

This work was supported partially by the National Natural Science Foundation of China (Grant Numbers 72074014 and 72004012). Our gratitude also goes to the anonymous reviewers and the editor for their valuable comments.

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Authors and Affiliations

  1. College of Economics and Management, Beijing University of Technology, Beijing, 100124, People’s Republic of China

    Shuo Xu, Ling Li & Liyuan Hao

  2. School of Economics and Management, Beijing Forestry University, Beijing, 100083, People’s Republic of China

    Xin An

  3. School of Information Resource Management, Renmin University of China, Beijing, 100872, People’s Republic of China

    Guancan Yang

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  1. Shuo Xu
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  2. Ling Li
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  3. Xin An
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  4. Liyuan Hao
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  5. Guancan Yang
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Correspondence to Xin An.

Appendix

Appendix

See Tables 11 and 12.

Table 11 Journals and abstract restrictions corresponding to the top 20% papers that appear most frequently
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Table 12 Two-tailed independent sample t-test results of 11 language complexity indicators
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Xu, S., Li, L., An, X. et al. An approach for detecting the commonality and specialty between scientific publications and patents. Scientometrics 126, 7445–7475 (2021). https://doi.org/10.1007/s11192-021-04085-9

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