%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 }