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<https://pod.rubendedecker.be/scholar/publications/vocabulary_hub_as_a_catalog_for_semantic_artifacts> a schema:ScholarlyArticle ;
    schema:associatedMedia <https://pod.rubendedecker.be/scholar/publications/2025/ESWC/demo/paper.pdf>;
    rdfs:comment "Presented at the SDS workshop at the Extended Semantic Web Conference 2026.";
    schema:name "The Vocabulary Hub as a Catalog of Semantic Artifacts for Discovery and Alignment of Datasets" ;
    schema:author  <https://pod.rubendedecker.be/profile/card#me>, <https://julianrojas.org/#me>,  <https://pietercolpaert.be/#me> ;
    schema:abstract 
"""
The EU Common Data Spaces initiative aims to enable secure, sovereign and interoperable data sharing across
organizational and national boundaries. However, the high heterogeneity of underlying data models and formats,
prevents semantic interoperability from being realized. Publishers can address this challenge by exposing
their internal knowledge by adopting continuous publishing models that reduce operational overhead for both
publishers and consumers. Yet for data consumers, costly alignments still remain a necessity when the semantics of
published datasets differ from their expected internal data models and schemas. Data spaces require mechanisms
to define, discover, and govern such alignments throughout their entire lifecycle, enabling eventual interoperability.
In this paper, we show that considering additional semantic artifacts as part of the vocabulary hub, namely dataset
profiles defining structural and semantic constraints, and profile alignments (e.g., in the form of SPARQL construct
queries), could provide consumers with a semantic entry point for dataset discovery and integration. We focus on
the interaction patterns enabled by these artifacts and present a demonstrator interface that supports profile‑based
discovery and alignment of datasets. We validate our approach through a use case from the DeployEMDS project,
focusing on the automatic discovery and alignment of traffic measurement datasets. The extended vocabulary hub
enables clients to discover datasets based on profile characteristics such as shapes, ontologies, and publishing data
models, while also identifying available alignment pathways toward target consumer data models. By anchoring
semantic alignments to profiles and exposing them as discoverable execution artifacts, the approach lowers the
technical barrier to semantic mediation and enables datasets to be consumed according to consumer‑specific data
models as realized through their vocabularies and schemas. Future work will focus on integrating this component
with existing data space connector implementations to further automate semantic interoperability by enabling
semantic and profile-based content negotiation for data exchanges.
""";
    schema:about <http://dbpedia.org/resource/Semantic_Web> ;
    schema:datePublished "2026-05-17"^^xsd:date ;
    schema:contributor knows: ;
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        a bibframe:Contribution, bflc:PrimaryContribution;
        bibframe:agent <https://pod.rubendedecker.be/profile/card#me>;
        vivo:rank 0
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        a bibframe:Contribution;
        bibframe:agent <https://julianrojas.org/#me>;
        vivo:rank 1
    ], [
        a bibframe:Contribution;
        bibframe:agent <pietercolpaert.be/#me>;
        vivo:rank 2
    ].