Data Integration for Open Data on the Web

Neumaier, Sebastian; Polleres, Axel; Steyskal, Simon; Umbrich, Jürgen

doi:10.1007/978-3-319-61033-7_1

Cited by 6 publications

(3 citation statements)

References 54 publications

(45 reference statements)

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“…Furthermore, it is possible to extend the DCAT-AP in order to meet local needs of implementers. In this context, the use of such extensions is indicated by [18], where the authors use several web vocabularies to generate metadata from Open Data portals which are available as Linked Data. Likewise, extensions have already been developed by the EC/ISA 2 Programme for the fields of geospatial [19] and statistical [20] data.…”

Section: Vocabularies and Metadata Standards For Open Data 121 Dcat-apmentioning

confidence: 99%

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Towards a Standard-based Open Data Ecosystem: Analysis of DCAT-AP use at National and European Level

Magnisalis¹,

Peristeras²,

Comet³

et al. 2022

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Section: Vocabularies and Metadata Standards For Open Data 121 Dcat-apmentioning

confidence: 99%

“…In order to analyse the real use of DCAT Given the high number of properties to analyse, the creation of queries was semiautomated using a spreadsheet 18 . Moreover, the queries were also transformed into URLs and results were automatically loaded.…”

Section: Analysis Of Dcat-ap Use Via the European Data Portalmentioning

confidence: 99%

Towards a Standard-based Open Data Ecosystem: Analysis of DCAT-AP use at National and European Level

Magnisalis¹,

Peristeras²,

Comet³

et al. 2022

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“…( 2) There are open data portals with collections or catalogs that index metadata and refer to the data set files. (3) There are freely accessible databases that were created and expanded jointly by users (Neumaier, Polleres, Steyskal, & Umbrich, 2017). Aside from the data sources, the metadata about data sets are modeled by means of various standards (e.g.…”

Section: Introductionmentioning

confidence: 99%

The data set knowledge graph: Creating a linked open data source for data sets

Färber

Lamprecht

2021

Quantitative Science Studies

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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.

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