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A linked open data representation of patents registered in the US from 2005–2017
Patents are widely used to protect intellectual property and a measure of innovation output. Each year, the USPTO grants over [Formula: see text] patents to individuals and companies all over the world. In fact, there were more than [Formula: see text] patent grants issued in the US in [Formula: see...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6278688/ https://www.ncbi.nlm.nih.gov/pubmed/30512011 http://dx.doi.org/10.1038/sdata.2018.279 |
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author | Hassan, Mofeed M. Zaveri, Amrapali Lehmann, Jens |
author_facet | Hassan, Mofeed M. Zaveri, Amrapali Lehmann, Jens |
author_sort | Hassan, Mofeed M. |
collection | PubMed |
description | Patents are widely used to protect intellectual property and a measure of innovation output. Each year, the USPTO grants over [Formula: see text] patents to individuals and companies all over the world. In fact, there were more than [Formula: see text] patent grants issued in the US in [Formula: see text]. However, accessing, searching and analyzing those patents is often still cumbersome and inefficient. To overcome those problems, Google indexes patents and converts them to Extensible Markup Language (XML) files using Optical Character Recognition (OCR) techniques. In this article, we take this idea one step further and provide semantically rich, machine-readable patents using the Linked Data principles. We have converted the data spanning [Formula: see text] years [Formula: see text] i.e. [Formula: see text] [Formula: see text] [Formula: see text] from XML to Resource Description Framework (RDF) format, conforming to the Linked Data principles and made them publicly available for re-use. This data can be integrated with other data sources in order to further simplify use cases such as trend analysis, structured patent search & exploration and societal progress measurements. We describe the conversion, publishing, interlinking process along with several use cases for the USPTO Linked Patent data. |
format | Online Article Text |
id | pubmed-6278688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-62786882018-12-05 A linked open data representation of patents registered in the US from 2005–2017 Hassan, Mofeed M. Zaveri, Amrapali Lehmann, Jens Sci Data Data Descriptor Patents are widely used to protect intellectual property and a measure of innovation output. Each year, the USPTO grants over [Formula: see text] patents to individuals and companies all over the world. In fact, there were more than [Formula: see text] patent grants issued in the US in [Formula: see text]. However, accessing, searching and analyzing those patents is often still cumbersome and inefficient. To overcome those problems, Google indexes patents and converts them to Extensible Markup Language (XML) files using Optical Character Recognition (OCR) techniques. In this article, we take this idea one step further and provide semantically rich, machine-readable patents using the Linked Data principles. We have converted the data spanning [Formula: see text] years [Formula: see text] i.e. [Formula: see text] [Formula: see text] [Formula: see text] from XML to Resource Description Framework (RDF) format, conforming to the Linked Data principles and made them publicly available for re-use. This data can be integrated with other data sources in order to further simplify use cases such as trend analysis, structured patent search & exploration and societal progress measurements. We describe the conversion, publishing, interlinking process along with several use cases for the USPTO Linked Patent data. Nature Publishing Group 2018-12-04 /pmc/articles/PMC6278688/ /pubmed/30512011 http://dx.doi.org/10.1038/sdata.2018.279 Text en Copyright © 2018, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor Hassan, Mofeed M. Zaveri, Amrapali Lehmann, Jens A linked open data representation of patents registered in the US from 2005–2017 |
title | A linked open data representation of patents registered in the US from 2005–2017 |
title_full | A linked open data representation of patents registered in the US from 2005–2017 |
title_fullStr | A linked open data representation of patents registered in the US from 2005–2017 |
title_full_unstemmed | A linked open data representation of patents registered in the US from 2005–2017 |
title_short | A linked open data representation of patents registered in the US from 2005–2017 |
title_sort | linked open data representation of patents registered in the us from 2005–2017 |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6278688/ https://www.ncbi.nlm.nih.gov/pubmed/30512011 http://dx.doi.org/10.1038/sdata.2018.279 |
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