Scholarly knowledge graphs are valuable sources of information in several
research fields. Despite the number of existing datasets related to
publications and researchers, resource quality, coverage and accessibility are
still limited. This article presents the Enhanced Microsoft Academic Knowledge
Graph, a large dataset of information about scientific publications and
involved entities, and the methods developed to build it. Data includes
geographical information, researchers' collaborative networks and movements
between institutions, academic-related metrics, and linguistic features. The
dataset merges information from several data sources and has high temporal and
spatial 7 coverage, allowing several use cases.
%0 Generic
%1 pollacci2022emakg
%A Pollacci, Laura
%D 2022
%K MAKG data dataset graph quality
%T EMAKG: An Enhanced Version Of The Microsoft Academic Knowledge Graph
%U http://arxiv.org/abs/2203.09159
%X Scholarly knowledge graphs are valuable sources of information in several
research fields. Despite the number of existing datasets related to
publications and researchers, resource quality, coverage and accessibility are
still limited. This article presents the Enhanced Microsoft Academic Knowledge
Graph, a large dataset of information about scientific publications and
involved entities, and the methods developed to build it. Data includes
geographical information, researchers' collaborative networks and movements
between institutions, academic-related metrics, and linguistic features. The
dataset merges information from several data sources and has high temporal and
spatial 7 coverage, allowing several use cases.
@misc{pollacci2022emakg,
abstract = {Scholarly knowledge graphs are valuable sources of information in several
research fields. Despite the number of existing datasets related to
publications and researchers, resource quality, coverage and accessibility are
still limited. This article presents the Enhanced Microsoft Academic Knowledge
Graph, a large dataset of information about scientific publications and
involved entities, and the methods developed to build it. Data includes
geographical information, researchers' collaborative networks and movements
between institutions, academic-related metrics, and linguistic features. The
dataset merges information from several data sources and has high temporal and
spatial 7 coverage, allowing several use cases.},
added-at = {2023-03-09T10:37:31.000+0100},
author = {Pollacci, Laura},
biburl = {https://www.bibsonomy.org/bibtex/2441b60e864cb7c027d2c885384ff1eef/parismic},
interhash = {cf9509427660f4b67e84ce1922ccc2ec},
intrahash = {441b60e864cb7c027d2c885384ff1eef},
keywords = {MAKG data dataset graph quality},
note = {cite arxiv:2203.09159},
timestamp = {2023-03-09T10:37:31.000+0100},
title = {EMAKG: An Enhanced Version Of The Microsoft Academic Knowledge Graph},
url = {http://arxiv.org/abs/2203.09159},
year = 2022
}