Abstract
Science and technology (S&T) linkages have been studied
extensively using patent and scientific publication
databases. Existing methods used to track S&T linkages,
such as analysis of non-patent literature (NPL) or
author-inventor matching offer a narrow window for
industry level analysis of the data. This paper examines
the application of a machine learning algorithm, namely
Latent Dirichlet Allocation, to detect the semantic
relationship between patent and scientific publication
corpus. The case of "Taxol", a cancer drug, is used to
illustrate the performance of the unsupervised algorithm
in clustering documents with similar topics. In total 26
475 documents retrieved from the Europe PMC database was
used a sample for the analysis. Qualitative analysis of
the clusters shows that the topic clustering algorithm is
valuable approach in detection of patent and publication
linkage.
Original language | English |
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Title of host publication | 2017 IEEE Technology & Engineering Management Conference (TEMSCON) |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Pages | 49-54 |
ISBN (Electronic) | 978-1-5090-1114-8 |
ISBN (Print) | 978-1-5090-1115-5 |
DOIs | |
Publication status | Published - 31 Jul 2017 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE Technology & Engineering Management Conference, TEMSCON 2017 - Santa Clara, United States Duration: 8 Jun 2017 → 10 Jun 2017 |
Conference
Conference | IEEE Technology & Engineering Management Conference, TEMSCON 2017 |
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Abbreviated title | TEMSCON 2017 |
Country/Territory | United States |
City | Santa Clara |
Period | 8/06/17 → 10/06/17 |
Keywords
- patents
- couplings
- drugs
- machine learning algorithms
- algorithm design and analysis
- classification algorithms
- analytical models
- topic modeling
- technology management
- taxol
- machine learning
- science and technology