ShExML: improving the usability of heterogeneous data mapping languages for first-time users

García-González, Herminio; Boneva, Iovka; Staworko, Sławek; Gayo, José Emilio Labra; Lovelle, Juan Manuel Cueva

doi:10.7717/peerj-cs.318

Cited by 28 publications

(24 citation statements)

References 21 publications

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“…H1 validation: I accept my hypothesis for RQ1, several approaches e.g. RML [6], SPARQL-Generate [11], or ShExML [14] exist for generating RDF from heterogeneous data. I identified several key factors e.g.…”

Section: Evaluation Planmentioning

confidence: 86%

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Balancing RDF Generation from Heterogeneous Data Sources

Assche

2022

The Semantic Web: ESWC 2022 Satellite Events

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Knowledge graphs in RDF are often generated from heterogeneous data sources to power services. However, knowledge graph generation is an unbalanced effort for producers compared to consumers of a knowledge graph. In this paper, I present my research about (i) investigating current RDF knowledge graph production and consumption approaches, and (ii) how to involve the consumer into a hybrid RDF generation approach to reduce the necessary resources for generating RDF for producers & consumers. I discuss the shortcomings of existing approaches for RDF generation from heterogeneous data sources (i.e., materialization and virtualization) and how I will address these: a Systematic Literature Review; an analysis and a set of guidelines for producers to select the right approach for an use case; and a combined hybrid approach to balance the producer's and consumer's effort in RDF generation. I already performed a Systematic Literature Review to get an overview of the existing approaches for RDF production from heterogeneous data sources. These results will be used to establish a set of producer guidelines, a benchmark to compare the current materialization and virtualization approaches, and evaluate the proposed hybrid approach. Thanks to my research, knowledge graph production and consumption will be more balanced and accessible to smaller companies and individuals. This way, they can focus on providing better services on top of a knowledge graph instead of being limited by the lack of computing resources to harvest enormous amounts of data from the Web and integrate it into a knowledge graph.

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Section: Evaluation Planmentioning

confidence: 86%

“…Besides R2RML based, SPARQL query language [10] based approaches exists as well, e.g., SPARQL-Generate [11], SPARQL-Anything [12], or XSPARQL [13]. They repurposed SPARQL to generate RDF from heterogeneous data sources; so does ShExML [14] which repurposes the constraint language ShEx [15].…”

Section: State Of the Artmentioning

confidence: 99%

Balancing RDF Generation from Heterogeneous Data Sources

Assche

2022

The Semantic Web: ESWC 2022 Satellite Events

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“…Finally, there are approaches that propose a smaller subset of the ShEx language. Shex-Lite 13 [2], ShExML [5], and ShExStatements belong to this category. ShExML is a language developed to integrate multiple heterogeneous data sources.…”

Section: Related Workmentioning

confidence: 99%

ShExStatements: Simplifying Shape Expressions for Wikidata

Samuel

2021

Companion Proceedings of the Web Conference 2021

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Wikidata recently supported entity schemas based on shape expressions (ShEx). They play an important role in the validation of items belonging to a multitude of domains on Wikidata. However, the number of entity schemas created by the contributors is relatively low compared to the number of WikiProjects. The past couple of years have seen attempts at simplifying the shape expressions and building tools for creating them. In this article, ShExStatements is presented with the goal of simplifying writing the shape expressions for Wikidata. CCS CONCEPTS• Software and its engineering → Software libraries and repositories; • Information systems → Web data description languages; Wikis.

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“…RML. There are other mapping languages that generate RDF graphs from heterogeneous sources: xR2RML [17], ShExML [10], and SPARQL-Generate [13], but we consider RML because it is the most broadly used. RML [8] generates RDF graphs from heterogeneous data sources, relying on triples maps, subject maps, predicate-object maps, term maps, references and iterators.…”

Section: Preliminariesmentioning

confidence: 99%

“…Astrea publishes both its code 8 as well as a catalogue of generated shapes from well-known ontologies online 9 . For each ontology in Astrea's catalogue, we generated mapping rules using a third party OWLto-RML tool 10 . From these generated mapping rules, we generate shapes using RML2SHACL's implementation.…”

Section: Comparison With Ontology-based Toolsmentioning

confidence: 99%

RML2SHACL: RDF Generation Taking Shape

Delva

Smedt

Min

et al. 2021

Proceedings of the 11th Knowledge Capture Conference

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RDF graphs are often generated by mapping data in other (semi-) structured data formats to RDF. Such mapped graphs have a repetitive structure defined by (i) the mapping rules and (ii) the schema of the input sources. However, this information is not exploited beyond its original scope. SHACL was recently introduced to model constraints that RDF graphs should validate. SHACL shapes and their constraints are either manually defined or derived from ontologies or RDF graphs. We investigate a method to derive the shapes and their constraints from mapping rules, allowing the generation of the RDF graph and the corresponding shapes in one step. In this paper, we present RML2SHACL: an approach to generate SHACL shapes that validate RDF graphs defined by RML mapping rules. RML2SHACL relies on our proposed set of correspondences between RML and SHACL constructs. RML2SHACL covers a large variety of RML constructs, as proven by generating shapes for the RML test cases. A comparative analysis shows that shapes generated by RML2SHACL are similar to shapes generated by ontology-based tools, with a larger focus on data value-based constraints instead of schema-based constraints. We also found that RML2SHACL has a faster execution time than data-graph based approaches for data sizes of 90MB and higher.

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ShExML: improving the usability of heterogeneous data mapping languages for first-time users

Cited by 28 publications

References 21 publications

Balancing RDF Generation from Heterogeneous Data Sources

Balancing RDF Generation from Heterogeneous Data Sources

ShExStatements: Simplifying Shape Expressions for Wikidata

RML2SHACL: RDF Generation Taking Shape

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