Attribution Schema

Rather than treating sources as simple text strings, a robust attribution schema models each source as a distinct entity with a unique identifier (e.g., a DOI, ORCID, or Wikidata Q-ID). This entity-centric approach enables AI systems to disambiguate sources—distinguishing between authors with identical names or publications with similar titles—and to link citations back to authoritative knowledge graphs.

• Resolves author ambiguity via ORCID identifiers
• Links publications to DOI infrastructure for persistent resolution
• Connects organizations to Wikidata or ISNI entries

An effective schema captures the full attribution chain, not just the immediate source. Using properties derived from the W3C PROV-O ontology, it records the sequence of derivation, revision, and quotation that connects a claim back to its original primary source. This includes modeling activities like prov:wasDerivedFrom, prov:wasAttributedTo, and prov:generatedAtTime to create a complete, auditable lineage.

• Traces claims through intermediary citations to the origin
• Records transformation activities (translation, summarization)
• Enables automated verification of citation integrity

To prevent tampering and ensure content authenticity, advanced attribution schemas incorporate cryptographic hash values of the source content. By binding a citation to a SHA-256 hash of the original document, the schema creates a verifiable fingerprint. If the source is altered, the hash mismatch immediately signals a provenance violation. This aligns with the C2PA content credentials standard.

• Embeds hash and hashAlgorithm properties
• Enables automated tamper-evident verification
• Supports integration with distributed provenance ledgers

Beyond identifying a source, a sophisticated schema qualifies the nature of the attribution. It distinguishes between a source that supports a claim, one that contradicts it, or one that serves as a primary reference. Properties can also encode a citation confidence score, reflecting the algorithmic certainty that the cited passage substantiates the generated statement.

• Qualifies roles: supporting, contradicting, primary
• Encodes confidence scores for AI self-assessment
• Enables downstream filtering by source authority vectors

Attribution schemas must account for the dynamic nature of digital content. They include versioning metadata and trusted timestamps to specify exactly which version of a source was cited. This prevents attribution drift, where a citation becomes invalid because the source has been updated or retracted. Properties like version and dateRetrieved anchor the citation to a specific point in time.

• Records version and dateRetrieved for precision
• Prevents drift from source updates or retractions
• Supports integration with trusted timestamping authorities

The assurance that a reference accurately represents its source material without alteration, misrepresentation, or contextomy (quoting out of context). Citation integrity is what attribution schema aims to preserve programmatically. Key mechanisms include:

• Cryptographic hashing of source content at ingestion time
• Span-level anchoring to specific passages
• Drift detection when sources are updated or retracted

Structured data, often expressed via the W3C PROV standard, that describes the origin, authorship, and transformation history of a digital asset. This metadata forms the semantic backbone of an attribution schema. Core PROV concepts include:

• Entity: the digital asset itself
• Agent: the person or system responsible for creation
• Activity: the process that generated or modified the entity

The process of anchoring an AI model's generated statements to specific, retrievable source documents. Attribution schema provides the structured vocabulary for source grounding to function at scale. Effective grounding requires document-level identifiers, passage-level pointers, and confidence calibration that signals how strongly a source supports a given claim.

An algorithmic method for assigning a quantitative score to a source-citation pair, reflecting the model's certainty that the source substantiates the claim. Attribution schema provides the structured fields that confidence models evaluate:

• Source authority vector: multi-dimensional trust assessment
• Semantic overlap: cosine similarity between claim and source passage
• Recency weighting: freshness of the cited information