Classifying patents based on their semantic content

Antonin Bergeaud, Yoann Potiron and Juste Raimbault

PLOS ONE, 2017, vol. 12, issue 4, 1-22

Abstract: In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information.

Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176310 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 76310&type=printable (application/pdf)

Related works:
Working Paper: Classifying Patents Based on their Semantic Content (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0176310

DOI: 10.1371/journal.pone.0176310

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().