A hybrid similarity measure method for patent portfolio analysis

Zhang, Y; Shang, L; Huang, L; Porter, AL; Zhang, G; Lu, J; Zhu, D

A hybrid similarity measure method for patent portfolio analysis

Zhang, Y

Shang, L Huang, L Porter, AL Zhang, G

Lu, J

Zhu, D

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Publication Type:: Journal Article
Citation:: Journal of Informetrics, 2016, 10 (4), pp. 1108 - 1130
Issue Date:: 2016-11-01

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Field	Value	Language
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301	en_US
dc.contributor.author	Shang, L	en_US
dc.contributor.author	Huang, L	en_US
dc.contributor.author	Porter, AL	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhu, D	en_US
dc.date.available	2020-05-25T19:03:21Z
dc.date.issued	2016-11-01	en_US
dc.identifier.citation	Journal of Informetrics, 2016, 10 (4), pp. 1108 - 1130	en_US
dc.identifier.issn	1751-1577	en_US
dc.identifier.uri	http://hdl.handle.net/10453/56175
dc.description.abstract	© 2016 Elsevier Ltd Similarity measures are fundamental tools for identifying relationships within or across patent portfolios. Many bibliometric indicators are used to determine similarity measures; for example, bibliographic coupling, citation and co-citation, and co-word distribution. This paper aims to construct a hybrid similarity measure method based on multiple indicators to analyze patent portfolios. Two models are proposed: categorical similarity and semantic similarity. The categorical similarity model emphasizes international patent classifications (IPCs), while the semantic similarity model emphasizes textual elements. We introduce fuzzy set routines to translate the rough technical (sub-) categories of IPCs into defined numeric values, and we calculate the categorical similarities between patent portfolios using membership grade vectors. In parallel, we identify and highlight core terms in a 3-level tree structure and compute the semantic similarities by comparing the tree-based structures. A weighting model is designed to consider: 1) the bias that exists between the categorical and semantic similarities, and 2) the weighting or integrating strategy for a hybrid method. A case study to measure the technological similarities between selected firms in China's medical device industry is used to demonstrate the reliability our method, and the results indicate the practical meaning of our method in a broad range of informetric applications.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140101366
dc.relation.ispartof	Journal of Informetrics	en_US
dc.relation.isbasedon	10.1016/j.joi.2016.09.006	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Information & Library Sciences	en_US
dc.title	A hybrid similarity measure method for patent portfolio analysis	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	10	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0807 Library and Information Studies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access	*
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

Abstract:

© 2016 Elsevier Ltd Similarity measures are fundamental tools for identifying relationships within or across patent portfolios. Many bibliometric indicators are used to determine similarity measures; for example, bibliographic coupling, citation and co-citation, and co-word distribution. This paper aims to construct a hybrid similarity measure method based on multiple indicators to analyze patent portfolios. Two models are proposed: categorical similarity and semantic similarity. The categorical similarity model emphasizes international patent classifications (IPCs), while the semantic similarity model emphasizes textual elements. We introduce fuzzy set routines to translate the rough technical (sub-) categories of IPCs into defined numeric values, and we calculate the categorical similarities between patent portfolios using membership grade vectors. In parallel, we identify and highlight core terms in a 3-level tree structure and compute the semantic similarities by comparing the tree-based structures. A weighting model is designed to consider: 1) the bias that exists between the categorical and semantic similarities, and 2) the weighting or integrating strategy for a hybrid method. A case study to measure the technological similarities between selected firms in China's medical device industry is used to demonstrate the reliability our method, and the results indicate the practical meaning of our method in a broad range of informetric applications.

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http://hdl.handle.net/10453/56175