A topic model analysis of science and technology linkages: A case study in pharmaceutical industry

Samira Ranaei; Arho Suominen; Ozgur Dedehayir

doi:10.1109/TEMSCON.2017.7998353

A topic model analysis of science and technology linkages: A case study in pharmaceutical industry

Samira Ranaei
, Arho Suominen
, Ozgur Dedehayir

Lappeenranta-Lahti University of Technology LUT
Queensland University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

15 Citations (Scopus)

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
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	https://doi.org/10.1109/TEMSCON.2017.7998353
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
Abbreviated title	TEMSCON 2017
Country/Territory	United States
City	Santa Clara
Period	8/06/17 → 10/06/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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

Access to Document

10.1109/TEMSCON.2017.7998353

Cite this

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title = "A topic model analysis of science and technology linkages: A case study in pharmaceutical industry",

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.",

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",

author = "Samira Ranaei and Arho Suominen and Ozgur Dedehayir",

note = "Funding Information: The Academy of Finland research supported this work with the grant (288609) Modeling Science and Technology Systems Through Massive Data Collections Publisher Copyright: {\textcopyright} 2017 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; IEEE Technology \& Engineering Management Conference, TEMSCON 2017, TEMSCON 2017 ; Conference date: 08-06-2017 Through 10-06-2017",

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doi = "10.1109/TEMSCON.2017.7998353",

language = "English",

isbn = "978-1-5090-1115-5",

pages = "49--54",

booktitle = "2017 IEEE Technology \& Engineering Management Conference (TEMSCON)",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

}

Ranaei, S, Suominen, A & Dedehayir, O 2017, A topic model analysis of science and technology linkages: A case study in pharmaceutical industry. in 2017 IEEE Technology & Engineering Management Conference (TEMSCON)., 7998353, IEEE Institute of Electrical and Electronic Engineers, pp. 49-54, IEEE Technology & Engineering Management Conference, TEMSCON 2017, Santa Clara, California, United States, 8/06/17. https://doi.org/10.1109/TEMSCON.2017.7998353

A topic model analysis of science and technology linkages: A case study in pharmaceutical industry. / Ranaei, Samira; Suominen, Arho; Dedehayir, Ozgur.
2017 IEEE Technology & Engineering Management Conference (TEMSCON). IEEE Institute of Electrical and Electronic Engineers, 2017. p. 49-54 7998353.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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T2 - IEEE Technology & Engineering Management Conference, TEMSCON 2017

AU - Ranaei, Samira

AU - Suominen, Arho

AU - Dedehayir, Ozgur

N1 - Funding Information: The Academy of Finland research supported this work with the grant (288609) Modeling Science and Technology Systems Through Massive Data Collections Publisher Copyright: © 2017 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2017/7/31

Y1 - 2017/7/31

N2 - 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.

AB - 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.

KW - patents

KW - couplings

KW - drugs

KW - machine learning algorithms

KW - algorithm design and analysis

KW - classification algorithms

KW - analytical models

KW - topic modeling

KW - technology management

KW - taxol

KW - machine learning

KW - science and technology

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DO - 10.1109/TEMSCON.2017.7998353

M3 - Conference article in proceedings

SN - 978-1-5090-1115-5

SP - 49

EP - 54

BT - 2017 IEEE Technology & Engineering Management Conference (TEMSCON)

PB - IEEE Institute of Electrical and Electronic Engineers

Y2 - 8 June 2017 through 10 June 2017

ER -

A topic model analysis of science and technology linkages: A case study in pharmaceutical industry

Abstract

Conference

UN SDGs

Keywords

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