Graph Semantics
Discussion
Graph semantics defines meaning through nodes, edges, identifiers, graph statements, graph patterns, graph vocabularies, and graph constraints.
A graph represents information by connecting things through relationships. In semantic graph work, the meaning comes from the governed identifiers, relationship names, vocabulary definitions, constraints, and traceability that give the graph structure its intended interpretation.
RDF provides a graph-based framework for representing information. An RDF graph contains RDF triples. Each RDF triple expresses one subject-predicate-object statement.
Graph semantics differs from graph structure. Graph structure identifies connected nodes and relationships. Graph semantics explains what those nodes and relationships mean within a defined domain.
Graph semantics also differs from ontology and OWL. An ontology captures selected semantic commitments for a domain. OWL provides an ontology language for the Semantic Web. A graph expresses semantic content when the graph preserves traceability to governed vocabulary, conceptual meaning, ontology content, rules, schemas, or other authoritative semantic sources.
Graph semantic content includes:
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Nodes
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Edges
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RDF triples
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Subject identifiers
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Predicate identifiers
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Object identifiers
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Graph patterns
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Relationship names
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Controlled vocabularies
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Named graphs
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Graph constraints
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Graph queries
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Inferred relationships
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Provenance relationships
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Traceability to domain meaning
A governed architecture preserves traceability from graph semantics to the conceptual model, vocabulary, ontology, schema, rule set, or other semantic source that establishes the intended meaning.
Definition
meaning defined through graph statements, graph relationships, graph patterns, identifiers, and constraints within a defined graph representation context
Source
DIDO Solutions usage, informed by RDF, graph data modeling, semantic graph practice, ontology practice, and semantic traceability practice.
Note
Graph semantics is not equivalent to graph structure. Graph structure connects nodes and relationships. Graph semantics establishes the meaning of those nodes and relationships through governed identifiers, vocabularies, ontologies, schemas, rules, constraints, and domain models.
Example
An FX graph represents information about Trade-123 using RDF triples.
| Subject | Predicate | Object | Semantic interpretation |
|---|---|---|---|
Trade-123 | hasCounterparty | LEI-5493001KJTIIGC8Y1R12 | the trade involves the identified legal entity as a counterparty |
Trade-123 | hasCurrencyPair | EUR/USD | the trade exchanges euros and US dollars |
Trade-123 | hasNotionalAmount | 1000000 | the trade has a defined notional amount |
Trade-123 | hasTradeDate | 2026-07-07 | the trade occurs on the stated trade date |
Trade-123 | hasSettlementDate | 2026-07-09 | the trade settles on the stated settlement date |
Trade-123 | hasLifecycleState | Confirmed | the trade has reached the confirmed lifecycle state |
Trade-123 | wasValidatedBy | Validator-Structural-01 | the trade has validation evidence from the structural validator |
The graph structure connects Trade-123 to counterparties, dates, amounts, lifecycle state, and validation evidence. The graph semantics comes from the governed meanings of hasCounterparty, hasCurrencyPair, hasNotionalAmount, hasTradeDate, hasSettlementDate, hasLifecycleState, and wasValidatedBy.
The same graph structure has different semantics when the predicates come from a different vocabulary or lack traceability to defined domain meaning. For example, a predicate named hasDate does not distinguish trade date, settlement date, effective date, or termination date. Graph semantics requires governed relationship names or additional constraints that preserve those distinctions.
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