Conformance Test Cases for the RDF Mapping Language (RML)

Heyvaert, Pieter; Chaves-Fraga, David; Priyatna, Freddy; Corcho, Óscar; Mannens, Erik; Verborgh, Ruben; Dimou, Anastasia

doi:10.1007/978-3-030-21395-4_12

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…With the different configurations and optimizations implemented in our engine, it can be applied to different use cases, efficiently handling the parameters that affect the creation of KGs and satisfying data integration requirements. In addition, SDM-RDFizer passed all proposed RML test-cases [30], 32 which means that our engine is fully compliant with the RML specification. Lastly, SDM-RDFizer v4.5.6 was awarded with the Task-specific prize at the Knowledge Graph Construction Workshop 2023 Challenge at ESWC 2023 [63].…”

Section: Main Characteristics Of Sdm-rdfizermentioning

confidence: 79%

“…Morph-KGC divides the triples maps into smaller triples maps and executes them in parallel (requirements RE3-Fragmented data and RE8-Mapping complexity). RMLMapper handles heterogeneous data well (requirements RE1-Heterogeneous data and RE6-Semi-structured data), and in the study conducted in Arenas-Guerrero et al [4], it presents the best level of conformance with respect to test cases defined by RML [30]. Finally, RocketRML was chosen because it has multiple specialized implementations focused on improving join execution between mappings [57] (requirement RE8-Mapping complexity).…”

Section: Conf9mentioning

confidence: 99%

“…The GitHub repository is also linked to the Zenodo platform, which provides a Digital Object Identifier (DOI) for the general repository 29 and also a DOI for each specific software release. 30 The engine is also available on the Python Package Index (PyPI), so it can be easily installed and integrated in other developments. 31 Finally, we also provide a docker image that deploys the SDM-RDFizer as a web service.…”

Section: Main Characteristics Of Sdm-rdfizermentioning

confidence: 99%

See 2 more Smart Citations

Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines1

Iglesias,

Vidal,

Collarana

et al. 2024

Self Cite

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The significant increase in data volume in recent years has prompted the adoption of knowledge graphs as valuable data structures for integrating diverse data and metadata. However, this surge in data availability has brought to light challenges related to standardization, interoperability, and data quality. Knowledge graph creation faces complexities from large data volumes, data heterogeneity, and high duplicate rates. This work addresses these challenges and proposes data management techniques to scale up the creation of knowledge graphs specified using the RDF Mapping Language (RML). These techniques are integrated into SDM-RDFizer, transforming it into a two-fold solution designed to address the complexities of generating knowledge graphs. Firstly, we introduce a reordering approach for RML triples maps, prioritizing the evaluation of the most selective maps first to reduce memory usage. Secondly, we employ an RDF compression strategy, along with optimized data structures and novel operators, to prevent the generation of duplicate RDF triples and optimize the execution of RML operators. We assess the performance of SDM-RDFizer through established benchmarks. The evaluation showcases the effectiveness of SDM-RDFizer compared to state-of-the-art RML engines, emphasizing the benefits of our techniques. Furthermore, the paper presents real-world projects where SDM-RDFizer has been utilized, providing insights into the advantages of declaratively defining knowledge graphs and efficiently executing these specifications using this engine.

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Section: Main Characteristics Of Sdm-rdfizermentioning

confidence: 79%

Section: Conf9mentioning

confidence: 99%

Section: Main Characteristics Of Sdm-rdfizermentioning

confidence: 99%

See 1 more Smart Citation

Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines1

Iglesias,

Vidal,

Collarana

et al. 2024

Self Cite

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“…The reason for using the intermediate YARRRML step instead of writing RML rules directly was because YARRRML, being human-readable, allows other researchers to extend the mapping documents with less effort and without needing expertise in semantic web formats. The RMLMapper [ 12 ], using both the extended JSON files and the RML document as input, produces a set of N -Triples for each paper. The authors concatenated all such N -Triple files to form a single KG.…”

Section: Resultsmentioning

confidence: 99%

Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

Chatterjee

Nardi

Oberije

et al. 2021

JPM

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Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was “covid-19 knowledge graph”. In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research.

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“…The RDF Mapping Language (RML) [28,29] has been proposed to override R2RML's limitation on the kind of supported data sources. For this, RML is designed as a superset of R2RML whose main feature consists of a vocabulary that defines a generic data source instead of the three kinds allowed in R2RML.…”

Section: Rmlmentioning

confidence: 99%

Mapping Spatiotemporal Data to RDF: A SPARQL Endpoint for Brussels

Vaisman

Chentout²

2019

IJGI

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This paper describes how a platform for publishing and querying linked open data for the Brussels Capital region in Belgium is built. Data are provided as relational tables or XML documents and are mapped into the RDF data model using R2RML, a standard language that allows defining customized mappings from relational databases to RDF datasets. In this work, data are spatiotemporal in nature; therefore, R2RML must be adapted to allow producing spatiotemporal Linked Open Data.Data generated in this way are used to populate a SPARQL endpoint, where queries are submitted and the result can be displayed on a map. This endpoint is implemented using Strabon, a spatiotemporal RDF triple store built by extending the RDF store Sesame. The first part of the paper describes how R2RML is adapted to allow producing spatial RDF data and to support XML data sources. These techniques are then used to map data about cultural events and public transport in Brussels into RDF. Spatial data are stored in the form of stRDF triples, the format required by Strabon. In addition, the endpoint is enriched with external data obtained from the Linked Open Data Cloud, from sites like DBpedia, Geonames, and LinkedGeoData, to provide context for analysis. The second part of the paper shows, through a comprehensive set of the spatial extension to SPARQL (stSPARQL) queries, how the endpoint can be exploited.

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Conformance Test Cases for the RDF Mapping Language (RML)

Cited by 9 publications

References 13 publications

Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines1

Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines1

Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

Mapping Spatiotemporal Data to RDF: A SPARQL Endpoint for Brussels

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