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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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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. |
format | Online Article Text |
id | pubmed-5405987 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT bergeaudantonin classifyingpatentsbasedontheirsemanticcontent AT potironyoann classifyingpatentsbasedontheirsemanticcontent AT raimbaultjuste classifyingpatentsbasedontheirsemanticcontent |