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Classifying patents based on their semantic content

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...

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Detalles Bibliográficos
Autores principales: Bergeaud, Antonin, Potiron, Yoann, Raimbault, Juste
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5405987/
https://www.ncbi.nlm.nih.gov/pubmed/28445550
http://dx.doi.org/10.1371/journal.pone.0176310
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author Bergeaud, Antonin
Potiron, Yoann
Raimbault, Juste
author_facet Bergeaud, Antonin
Potiron, Yoann
Raimbault, Juste
author_sort Bergeaud, Antonin
collection PubMed
description 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.
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spelling pubmed-54059872017-05-14 Classifying patents based on their semantic content Bergeaud, Antonin Potiron, Yoann Raimbault, Juste PLoS One Research Article 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. Public Library of Science 2017-04-26 /pmc/articles/PMC5405987/ /pubmed/28445550 http://dx.doi.org/10.1371/journal.pone.0176310 Text en © 2017 Bergeaud et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bergeaud, Antonin
Potiron, Yoann
Raimbault, Juste
Classifying patents based on their semantic content
title Classifying patents based on their semantic content
title_full Classifying patents based on their semantic content
title_fullStr Classifying patents based on their semantic content
title_full_unstemmed Classifying patents based on their semantic content
title_short Classifying patents based on their semantic content
title_sort classifying patents based on their semantic content
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5405987/
https://www.ncbi.nlm.nih.gov/pubmed/28445550
http://dx.doi.org/10.1371/journal.pone.0176310
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