Trust Score Ontology

Defines a taxonomy of trust where classes like TrustSignal, AuthorityVector, and ReputationDecayFunction are organized into superclass-subclass relationships using OWL or RDF Schema. This enables automated reasoning about signal types—for example, inferring that a CitationIntegrityScore is-a QualitySignal is-a TrustSignal—allowing scoring engines to apply consistent aggregation rules across diverse inputs.

Specifies object properties that link entities with defined semantics:

• ex:hasTrustScore — connects an entity to its metric
• ex:derivedFrom — links a score to its source signals
• ex:attestsTo — binds a cryptographic proof to content
• ex:decaysVia — associates a signal with its decay function These relationships form the inference substrate that allows graph-based trust propagation algorithms to traverse and compute transitive trust.

Encodes SHACL shapes or OWL restrictions that enforce data integrity:

• A TrustScore must have exactly one numericValue
• ConfidenceWeighting requires a confidenceInterval with lowerBound and upperBound
• BayesianTrustNetwork updates require a priorProbability These machine-enforceable rules prevent malformed trust data from entering the aggregation pipeline, ensuring garbage-in prevention at the schema level.

By mapping proprietary trust signals to a shared canonical ontology, disparate scoring platforms achieve semantic alignment. A CredibilityIndex from one vendor and a TrustRank from another can be compared and fused because both are declared as subclasses of CompositeTrustMetric. This ontology serves as the Rosetta Stone for trust data exchange, enabling federated trust scoring across organizational boundaries without manual signal mapping.

Incorporates time-bound and domain-scoped class definitions:

• TemporalTrustSnapshot captures a score at a specific timestamp
• DomainSpecificAuthority restricts an AuthorityVector to a defined TopicDomain
• ReputationDecayFunction includes halfLife as a datatype property This scoping prevents cross-domain contamination—an entity's high trust score in MedicalImaging does not improperly propagate to FinancialFraudDetection without explicit domain alignment.

Links every trust assertion to its provenance chain using properties like prov:wasGeneratedBy and prov:wasDerivedFrom from the W3C PROV-O ontology. This creates an immutable audit trail showing:

• Which algorithm produced the score
• What input signals were consumed
• When the computation occurred
• Which entity performed the evaluation This binding is essential for trust score governance and regulatory compliance audits.

The formal data structure definition that standardizes the attributes and data types of a trust score object for interoperability between systems.

• Typically defined using Protocol Buffers or JSON Schema
• Includes fields for score value, confidence interval, timestamp, and provenance
• Enables cross-platform exchange without semantic ambiguity
• Example: A schema might mandate a credibility_index float between 0.0 and 1.0 with a required evidence_chain array

The algorithmic mechanism by which a trust score is transitively assigned from a known, high-authority entity to connected or cited entities within a reputation graph.

• Relies on graph traversal algorithms to distribute trust along edges
• Common models include Trust Rank and Advogato flow metrics
• Attenuation factors prevent infinite propagation across long chains
• Critical for bootstrapping trust for new entities with no direct history

A time-dependent mathematical formula that systematically reduces the weight of older trust signals to prevent stale authority from indefinitely influencing a current trust score.

• Often implemented as exponential decay: weight = e^(-λt)
• Half-life parameters are tuned per signal type—citations may decay slower than social endorsements
• Prevents legacy entities from maintaining dominance without ongoing positive signals
• Essential for dynamic environments where relevance is time-sensitive

The rigorous offline and online testing methodology used to confirm that a trust scoring model accurately predicts trustworthiness against a held-out, ground-truth dataset.

• Holdout sets of manually vetted trustworthy and untrustworthy entities serve as benchmarks
• Metrics include precision, recall, and area under the ROC curve
• Online validation uses A/B testing to measure real-world impact on downstream decisions
• Without validation, trust scores risk encoding systemic bias or manipulation

A probabilistic graphical model that uses Bayesian inference to update an entity's trustworthiness score dynamically as new, potentially uncertain evidence is observed.

• Nodes represent entities and evidence variables; edges encode conditional dependencies
• Prior trust distributions are updated via Bayes' theorem with each new signal
• Naturally handles uncertainty and conflicting evidence sources
• Provides a mathematically rigorous alternative to weighted sum aggregation

The organizational framework of policies, auditing procedures, and ethical oversight committees that manage the lifecycle of algorithmic trust systems.

• Defines appeal processes for entities contesting their assigned scores
• Mandates regular bias audits across demographic and topical dimensions
• Establishes version control and rollback procedures for model updates
• Aligns with regulatory requirements such as the EU AI Act's transparency mandates