Bergeaud, A;
Potiron, Y;
Raimbault, J;
(2017)
Classifying patents based on their semantic content.
PLoS One
, 12
(4)
, Article e0176310. 10.1371/journal.pone.0176310.
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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.
Type: | Article |
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Title: | Classifying patents based on their semantic content |
Location: | United States |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1371/journal.pone.0176310 |
Publisher version: | http://doi.org/10.1371/journal.pone.0176310 |
Language: | English |
Additional information: | Copyright: © 2017 Bergeaud et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Keywords: | Data Mining, Databases, Factual, Humans, Models, Statistical, Patents as Topic |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis |
URI: | https://discovery.ucl.ac.uk/id/eprint/10070402 |
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