Stage-oe-small.jpg

Article3258: Unterschied zwischen den Versionen

Aus Aifbportal
Wechseln zu:Navigation, Suche
(Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorNachname=Färber |ErsterAutorVorname=Michael }} {{Publikation Author |Rank=2 |Author=David Lamprecht }} {{Article |Refer…“)
 
 
(Eine dazwischenliegende Version desselben Benutzers wird nicht angezeigt)
Zeile 13: Zeile 13:
 
|Journal=Quantitative Science Studies
 
|Journal=Quantitative Science Studies
 
|Publisher=MIT Press
 
|Publisher=MIT Press
 +
}}
 +
{{Publikation Dataset
 +
|Dataset=Data Set Knowledge Graph
 
}}
 
}}
 
{{Publikation Details
 
{{Publikation Details
 
|Abstract=Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape. However, although the number of data sets has increased remarkably in recent years, these knowledge graphs do not primarily focus on data sets but rather associated entities such as publications. Moreover, publicly available data set knowledge graphs do not systematically contain links to the publications in which the data sets are mentioned. In this paper, we present an approach for constructing an RDF knowledge graph that fulfills these mentioned criteria. Our data set knowledge graph, DSKG, is publicly available at http://dskg.org and contains metadata of data sets for all scientific disciplines. To ensure high data quality of the DSKG, we first identify suitable raw data set collections for creating the DSKG. We then establish links between the data sets and publications modeled in the Microsoft Academic Knowledge Graph that mention these data sets. As the author names of data sets can be ambiguous, we develop and evaluate a method for author name disambiguation and enrich the knowledge graph with links to ORCID. Overall, our knowledge graph contains more than 2,000 data sets with associated properties, as well as 814,000 links to 635,000 scientific publications. It can be used for a variety of scenarios, facilitating advanced data set search systems and new ways of measuring and awarding the provisioning of data sets.
 
|Abstract=Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape. However, although the number of data sets has increased remarkably in recent years, these knowledge graphs do not primarily focus on data sets but rather associated entities such as publications. Moreover, publicly available data set knowledge graphs do not systematically contain links to the publications in which the data sets are mentioned. In this paper, we present an approach for constructing an RDF knowledge graph that fulfills these mentioned criteria. Our data set knowledge graph, DSKG, is publicly available at http://dskg.org and contains metadata of data sets for all scientific disciplines. To ensure high data quality of the DSKG, we first identify suitable raw data set collections for creating the DSKG. We then establish links between the data sets and publications modeled in the Microsoft Academic Knowledge Graph that mention these data sets. As the author names of data sets can be ambiguous, we develop and evaluate a method for author name disambiguation and enrich the knowledge graph with links to ORCID. Overall, our knowledge graph contains more than 2,000 data sets with associated properties, as well as 814,000 links to 635,000 scientific publications. It can be used for a variety of scenarios, facilitating advanced data set search systems and new ways of measuring and awarding the provisioning of data sets.
 
|Download=DSKG_QSS2021_v0.pdf
 
|Download=DSKG_QSS2021_v0.pdf
 +
|Link=https://doi.org/10.1162/qss_a_00161
 +
|DOI Name=10.1162/qss_a_00161
 
|Forschungsgruppe=Web Science
 
|Forschungsgruppe=Web Science
 
}}
 
}}

Aktuelle Version vom 9. November 2021, 15:54 Uhr


The Data Set Knowledge Graph: Creating a Linked Open Data Source for Data Sets


The Data Set Knowledge Graph: Creating a Linked Open Data Source for Data Sets



Veröffentlicht: 2021

Journal: Quantitative Science Studies


Verlag: MIT Press


Referierte Veröffentlichung

BibTeX





Kurzfassung
Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape. However, although the number of data sets has increased remarkably in recent years, these knowledge graphs do not primarily focus on data sets but rather associated entities such as publications. Moreover, publicly available data set knowledge graphs do not systematically contain links to the publications in which the data sets are mentioned. In this paper, we present an approach for constructing an RDF knowledge graph that fulfills these mentioned criteria. Our data set knowledge graph, DSKG, is publicly available at http://dskg.org and contains metadata of data sets for all scientific disciplines. To ensure high data quality of the DSKG, we first identify suitable raw data set collections for creating the DSKG. We then establish links between the data sets and publications modeled in the Microsoft Academic Knowledge Graph that mention these data sets. As the author names of data sets can be ambiguous, we develop and evaluate a method for author name disambiguation and enrich the knowledge graph with links to ORCID. Overall, our knowledge graph contains more than 2,000 data sets with associated properties, as well as 814,000 links to 635,000 scientific publications. It can be used for a variety of scenarios, facilitating advanced data set search systems and new ways of measuring and awarding the provisioning of data sets.

Download: Media:DSKG_QSS2021_v0.pdf
Weitere Informationen unter: Link
DOI Link: 10.1162/qss_a_00161


Verknüpfte Datasets

Data Set Knowledge Graph


Forschungsgruppe

Web Science


Forschungsgebiet

Wissensrepräsentation, Digitale Bibliotheken, Knowledge Discovery, Semantic Web