Semantic units: organizing knowledge graphs into semantically meaningful units of representation

dc.bibliographicCitation.articleNumber7
dc.bibliographicCitation.firstPage7
dc.bibliographicCitation.issue1
dc.bibliographicCitation.journalTitleJournal of Biomedical Semantics
dc.bibliographicCitation.volume15
dc.contributor.authorVogt, Lars
dc.contributor.authorKuhn, Tobias
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2025-02-26T09:28:34Z
dc.date.available2025-02-26T09:28:34Z
dc.date.issued2024
dc.description.abstractBackground: In today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles—ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. Results: We introduce “semantic units” as a conceptual solution, although currently exemplified only in a limited prototype. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs by adding another layer of triples on top of the conventional data layer. Semantic units and their subgraphs are represented by their own resource that instantiates a corresponding semantic unit class. We distinguish statement and compound units as basic categories of semantic units. A statement unit is the smallest, independent proposition that is semantically meaningful for a human reader. Depending on the relation of its underlying proposition, it consists of one or more triples. Organizing a knowledge graph into statement units results in a partition of the graph, with each triple belonging to exactly one statement unit. A compound unit, on the other hand, is a semantically meaningful collection of statement and compound units that form larger subgraphs. Some semantic units organize the graph into different levels of representational granularity, others orthogonally into different types of granularity trees or different frames of reference, structuring and organizing the knowledge graph into partially overlapping, partially enclosed subgraphs, each of which can be referenced by its own resource. Conclusions: Semantic units, applicable in RDF/OWL and labeled property graphs, offer support for making statements about statements and facilitate graph-alignment, subgraph-matching, knowledge graph profiling, and for management of access restrictions to sensitive data. Additionally, we argue that organizing the graph into semantic units promotes the differentiation of ontological and discursive information, and that it also supports the differentiation of multiple frames of reference within the graph.eng
dc.description.fondsTIB_Fonds
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/18574
dc.identifier.urihttps://doi.org/10.34657/17593
dc.language.isoeng
dc.publisherLondon : BioMed Central
dc.relation.doihttps://doi.org/10.1186/s13326-024-00310-5
dc.relation.essn2041-1480
dc.rights.licenseCC BY 4.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subject.ddc570
dc.subject.ddc610
dc.subject.otherFAIR data and metadataeng
dc.subject.otherGranularity treeeng
dc.subject.otherGraph organizationeng
dc.subject.otherKnowledge grapheng
dc.subject.otherOWLeng
dc.subject.otherRDFeng
dc.subject.otherRepresentational granularityeng
dc.subject.otherSemantic uniteng
dc.titleSemantic units: organizing knowledge graphs into semantically meaningful units of representationeng
dc.typeArticle
dc.typeText
tib.accessRightsopenAccess
wgl.contributorTIB
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