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Dataset . 2020
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Dataset . 2019
License: CC BY SA
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ZENODO
Dataset . 2019
License: CC BY SA
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY SA
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY SA
Data sources: ZENODO
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ConceptNet 5.x Raw Data

Authors: Speer, Robyn;

ConceptNet 5.x Raw Data

Abstract

This archive contains the raw data that ConceptNet 5 is built from. More information about ConceptNet is available at http://conceptnet.io. If you use ConceptNet as part of another work, you must attribute ConceptNet and you must not restrict its license terms. For more license information: https://creativecommons.org/licenses/by-sa/4.0/ ConceptNet has been developed by: * The MIT Media Lab, through various groups at different times: - Commonsense Computing - Software Agents - Digital Intuition * The Commonsense Computing Initiative, a worldwide collaboration with contributions from: - National Taiwan University - Universidade Federal de São Carlos - Hokkaido University - Tilburg University - Nihon Unisys Labs - Dentsu Inc. - Kyoto University - Yahoo Research Japan * Luminoso Technologies, Inc. Significant amounts of data were imported from: * WordNet, a project of Princeton University * Wikipedia and Wiktionary, collaborative projects of the Wikimedia Foundation * Luis von Ahn's "Games with a Purpose" * DBPedia * OpenCyc * JMDict, by Jim Breen ConceptNet also takes input from these sources of distributional word embeddings: ConceptNet takes input from these sources of pre-computed distributional word embeddings: - GloVe: Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation. https://nlp.stanford.edu/projects/glove/ - word2vec: Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In Computing Research Repository. http://dblp.org/rec/bib/journals/corr/abs-1301-3781 - fastText: Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016. Enriching Word Vectors with Subword Information. http://fasttext.cc Here is a short, incomplete list of people who have made significant contributions to the development of ConceptNet as a data resource, roughly in order of appearance: * Push Singh * Catherine Havasi * Hugo Liu * Hyemin Chung * Robyn Speer * Ken Arnold * Yen-Ling Kuo * Naoki Otani

Keywords

knowledge graph, semantic network, ConceptNet, multilingual

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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visibility
download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
0
Average
Average
Average
23
164