Challenges in RDF Validation

Gayo, José Emilio Labra; García-González, Herminio; Fernández-Álvarez, Daniel; Prud’hommeaux, Eric

doi:10.1007/978-3-030-06149-4_6

Cited by 11 publications

(10 citation statements)

References 29 publications

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“…In addition, experiments relied on two ontologies developed in the context of the European Projects VICINITY and DELTA, namely: the VICIN-ITY ontology 12 and the DELTA ontology 13 . All the shapes generated during both experiments using Astrea were manually validated in term of syntax correctness using the SHACL playground 14 .…”

Section: Methodsmentioning

confidence: 99%

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Astrea: Automatic Generation of SHACL Shapes from Ontologies

Cimmino

Fernández-Izquierdo

García‐Castro

2020

Lecture Notes in Computer Science

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Knowledge Graphs (KGs) that publish RDF data modelled using ontologies in a wide range of domains have populated the Web. The SHACL language is a W3C recommendation that has been endowed to encode a set of either value or model data restrictions that aim at validating KG data, ensuring data quality. Developing shapes is a complex and time consuming task that is not feasible to achieve manually. This article presents two resources that aim at generating automatically SHACL shapes for a set of ontologies: (1) Astrea-KG, a KG that publishes a set of mappings that encode the equivalent conceptual restrictions among ontology constraint patterns and SHACL constraint patterns, and (2) Astrea, a tool that automatically generates SHACL shapes from a set of ontologies by executing the mappings from the Astrea-KG. These two resources are openly available at Zenodo, GitHub, and a web application. In contrast to other proposals, these resources cover a large number of SHACL restrictions producing both value and model data restrictions, whereas other proposals consider only a limited number of restrictions or focus only on value or model restrictions.

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Section: Methodsmentioning

confidence: 99%

“…Due to the growth of these public available KGs, the W3C has promoted a recommendation called SHACL (Shapes Constraint Language) to validate the RDF graphs [2]. In the last years KGs validation by means of SHACL shapes has gained momentum and has become a relevant research topic [14].…”

Section: Introductionmentioning

confidence: 99%

Astrea: Automatic Generation of SHACL Shapes from Ontologies

Cimmino

Fernández-Izquierdo

García‐Castro

2020

Lecture Notes in Computer Science

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“…More details about ShEx and SHACL can be found in the book by Labra Gayo et al [294]. A recently proposed language that can be used as a common basis for both ShEx and SHACL reveals their similarities and differences [293]. A similar notion of schema has been proposed by Angles [13] for property graphs.…”

Section: Santiagomentioning

confidence: 99%

Knowledge Graphs

Hogan,

Blomqvist,

Cochez

et al. 2020

Preprint

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In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

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“…As part of our work, during the BioHackathon we worked on identifying a common subset of ShEx that could be used as the basis for the generation of RDF data models documentation, which can later be converted to JSON schema, ShEx or SHACL. Although full interoperability between those languages is not feasible, we consider that a subset language could be defined that could handle the most common cases (Labra-Gayo, García-González, Fernández-Alvarez, & Prud'hommeaux, 2019). Through CD2H's ("CD2H," n.d.) Data Discovery Engine ("CTSA Data Discovery Engine," n.d.) project, we previously developed a web-based tool called Schema Playground ("CTSA Data Discovery Engine Schema playground," n.d.) to facilitate the schema visualization, hosting and extension.…”

Section: Schema Conversion Across Validation Approachesmentioning

confidence: 99%

Data validation and schema interoperability

Castro¹,

Bolleman²,

Dumontier³

et al. 2020

Preprint

Self Cite

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Validating RDF data becomes necessary in order to ensure data compliance against the conceptualization model it follows, e.g., schema or ontology behind the data, and improve data consistency and completeness. There are different approaches to validate RDF data, for instance, JSON schema, particularly for data in JSONLD format, as well as Shape Expression and Shapes Constraint Language, which can be used with other serialization as well, e.g., RDF/XML or Turtle. Currently, no validation approach is prevalent regarding others, selection commonly depends on data characteristics, background knowledge and personal preferences . In some cases, the approaches are interchangeable; however, that is not always the case, making it necessary to identify a subset among them that can be seamlessly translated from one to another. During the NBDC/DBCLS 2019 BioHackathon, we worked on a variety of topics related to RDF data validation, including (i) development of ShEx shapes for a number of datasets, (ii) development of a tool to semi-automatically create ShEx shapes, (iii) improvements to the RDFShape tool, and (iv) enabling validation schema conversion from one format to the other. Here we report on our BioHackathon achievements.

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Challenges in RDF Validation

Cited by 11 publications

References 29 publications

Astrea: Automatic Generation of SHACL Shapes from Ontologies

Astrea: Automatic Generation of SHACL Shapes from Ontologies

Knowledge Graphs

Data validation and schema interoperability

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