Generation of Declarative Transformations from Semantic Models. Alexiev, V. In European Data Conference on Reference Data and Semantics (ENDORSE 2023), pages 33, 42-59, March, 2023. European Commission: Directorate-General for Informatics, Publications Office of the European Union. ![Generation of Declarative Transformations from Semantic Models [link]](https://bibbase.org/img/filetypes/link.svg "link")
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Slides Video Proceedings doi abstract bibtex 1 download The daily work of the Knowledge Graph Solutions group at Ontotext involves KG building activities such as investigating data standards and datasets, ontology engineering, harmonizing data through semantic models, converting or virtualizing data to semantic form, entity matching, semantic text enrichment, etc. Semantic pipelines have a variety of desirable properties, of which maintainability and consistency of the various artefacts are some of the most important ones. Despite significant recent progress (eg in the KG Building W3C community group), semantic conversion still remains one of the difficult steps. We favor generation of semantic transformations from semantic models that are both sufficiently precise, easily understandable, can be used to generate diagrams, and are valid RDF to allow processing with RDF tools. We call this approach "RDF by Example" and have developed a set of open source tools at https://github.com/VladimirAlexiev/rdf2rml. This includes "rdfpuml" for generating diagrams, "rdf2rml" for generating R2RML for semantization of relational data and ONTOP virtualization, "rdf2sparql" for semantization of tabular data with Ontotext Refine or TARQL. We describe our approach and illustrate it with complex and high-performance transformations in a variety of domains, such as company data and NIH research grants.
@InProceedings{Alexiev-ENDORSE-2023,
author = {Vladimir Alexiev},
title = {{Generation of Declarative Transformations from Semantic Models}},
booktitle = {{European Data Conference on Reference Data and Semantics (ENDORSE 2023)}},
year = 2023,
pages = {33, 42-59},
month = mar,
organization = {European Commission: Directorate-General for Informatics, Publications Office of the European Union},
url = {https://drive.google.com/open?id=1Cq5o9th_P812paqGkDsaEomJyAmnypkD},
url_PPT = {https://docs.google.com/presentation/d/1JCMQEH8Tw_F-ta6haIToXMLYJxQ9LRv6/edit},
url_Slides = {https://op.europa.eu/documents/10157494/12134844/DAY1-TRACK2-16.35-16.50-VladimirAlexiev_FORPUB.pdf},
url_Video = {https://youtu.be/yL5nI_3ccxs},
keywords = {semantic model, semantic data integration, ETL, semantic conversion, declarative approaches, PlantUML, R2RML, generation, model-driven, RDF by Example, rdfpuml, rdf2rml},
isbn = {978-92-78-43682-7},
doi = {10.2830/343811},
annote = {Catalogue number: OA-04-23-743-EN-N},
date = {2023-08-04},
url_proceedings= {https://op.europa.eu/en/publication-detail/-/publication/4db67b35-34df-11ee-bdc3-01aa75ed71a1},
abstract = {The daily work of the Knowledge Graph Solutions group at Ontotext involves KG building activities such as investigating data standards and datasets, ontology engineering, harmonizing data through semantic models, converting or virtualizing data to semantic form, entity matching, semantic text enrichment, etc. Semantic pipelines have a variety of desirable properties, of which maintainability and consistency of the various artefacts are some of the most important ones. Despite significant recent progress (eg in the KG Building W3C community group), semantic conversion still remains one of the difficult steps. We favor generation of semantic transformations from semantic models that are both sufficiently precise, easily understandable, can be used to generate diagrams, and are valid RDF to allow processing with RDF tools. We call this approach "RDF by Example" and have developed a set of open source tools at https://github.com/VladimirAlexiev/rdf2rml. This includes "rdfpuml" for generating diagrams, "rdf2rml" for generating R2RML for semantization of relational data and ONTOP virtualization, "rdf2sparql" for semantization of tabular data with Ontotext Refine or TARQL. We describe our approach and illustrate it with complex and high-performance transformations in a variety of domains, such as company data and NIH research grants.},
}
Downloads: 1
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