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

Shuo Xu (), Ling Li (), Xin An (), Liyuan Hao () and Guancan Yang ()
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Shuo Xu: Beijing University of Technology
Ling Li: Beijing University of Technology
Xin An: Beijing Forestry University
Liyuan Hao: Beijing University of Technology
Guancan Yang: Renmin University of China

Scientometrics, 2021, vol. 126, issue 9, No 7, 7445-7475

Abstract: 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.

Keywords: Linguistic characteristics; Stopword identification; Multi-collection topic model; Scientific publication; Patent (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11192-021-04085-9

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