The New Microdata Information System (MISSY) - Integration of DDI-based Data Models, an Open-Source Software Architecture, and Independent Persistence Service Implementations (EDDI 2013) [2013.12]

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The New Microdata Information System (MISSY) - Integration of DDI-based Data Models, an Open-Source Software Architecture, and Independent Persistence Service Implementations

Technology

More from Dr.-Ing. Thomas Hartmann

For research institutes, data libraries, and data archives, RDF data validation according to predefined constraints is a much sought-after feature, particularly as this is taken for granted in the XML world. Based on our work in the DCMI RDF Application Profiles Task Group and in cooperation with the W3C Data Shapes Working Group, we identified and published by today 81 types of constraints that are required by various stakeholders for data applications. In this paper, in collaboration with several domain experts we formulate 115 constraints on three different vocabularies (DDI-RDF, QB, and SKOS) and classify them according to (1) the severity of an occurring violation and (2) the complexity of the constraint expression in common constraint languages. We evaluate the data quality of 15,694 data sets (4.26 billion triples) of research data for the social, behavioral, and economic sciences obtained from 33 SPARQL endpoints. Based on the results, we formulate several findings to direct the further development of constraint languages.

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Recently, RDF validation as a research field gained speed due to common needs of data practitioners. A typical example is the library domain that co-developed and adopted Linked Data principles very early. Although, there are multiple constraint languages (having different syntaxes and semantics) which can be used to express RDF constraints such as cardinality restrictions, there is no constraint language which can be seen as the standard. The five most promising ones on being the standard are Description Set Profiles (DSP), Resource Shapes (ReSh), Shape Expressions (ShEx), the SPARQL Inferencing Notation (SPIN), and the Web Ontology Language (OWL 2). SPARQL is generally seen as the method of choice to validate RDF data according to certain constraints. We use SPIN, a SPARQL-based way to formulate and check constraints, as basis to define a validation environment (available at http://purl.org/net/rdfval-demo) to validate RDF data according to constraints expressed by arbitrary constraint languages. Additionally, the RDF Validator can be used to validate RDF data to ensure correct syntax and intended semantics of vocabularies such as Disco, Data Cube, DCAT, and SKOS. We present how to express typical RDF constraints by multiple constraint languages and how to actually validate RDF data conforming to these constraints using the RDF Validator. The workshop participants are encouraged to use the RDF Validator during this session (only an internet browser is needed) in order to express RDF constraints they need for their individual purposes.

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2014.12 - Let's Disco - 2 (EDDI 2014)

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2014.12 - Let's Disco (EDDI 2014)

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2014.10 - How to Formulate and Validate Constraints (DC 2014)

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2014.10 - Towards Description Set Profiles for RDF Using SPARQL as Intermedia...

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2014.10 - Requirements on RDF Constraint Formulation and Validation (DC 2014)

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The Next Generation of the Microdata Information System MISSY - An Integrated...

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Towards the Discovery of Person-Level Data (SemStats, ISWC 2013) [2013.10]

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2013.05 - IASSIST 2013 - 3

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2013.05 - IASSIST 2013 - 2

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2013.05 - IASSIST 2013

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2016.02 - Validating RDF Data Quality using Constraints to Direct the Develop...

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