Attribution Schema

The foundational vocabulary for attribution leverages existing Schema.org types like CreativeWork, Article, and WebPage. Key properties include:

• author and creator: Link to Person or Organization entities to establish who produced the content.
• datePublished and dateModified: Provide the temporal context necessary for a model to assess recency and version history.
• license: A URL pointing to the specific legal terms under which the content can be used, critical for automated rights management.
• isBasedOn and citation: Explicitly link a work to its source materials, creating a machine-traversable provenance graph.

The citation property is the primary mechanism for encoding bibliographic references in structured data. It accepts either a CreativeWork or a Text value.

• When used on an Article, it can point to the academic papers, datasets, or reports that support its claims.
• A generative model parsing a page can extract this linked data to populate its own citations, moving beyond unreliable surface-level text extraction.
• This creates a direct, verifiable link between a claim and its source, forming the basis of source grounding.

The W3C's Secure Data Distribution (SDD) framework, while broader, provides a robust mechanism for attribution. It allows for:

• Cryptographic hashing of content to create a unique content fingerprint.
• Digital signatures over the metadata, proving the attribution statement itself hasn't been tampered with.
• This moves beyond simple text-based citation to a cryptographically verifiable attestation of origin, which is essential for establishing trust in high-stakes environments like news and scientific publishing.

For images, video, and audio, the IPTC Photo Metadata Standard is the industry norm for embedding attribution. Key fields include:

• Creator, Credit Line, and Source fields explicitly identify the rights holder.
• Copyright Notice provides the legal statement.
• Digital Source Type is a newer, critical field that identifies whether the media was captured by a camera, generated by AI, or is a composite, directly addressing synthetic media provenance.
• This structured metadata travels with the asset file, ensuring attribution persists across platforms.

The Coalition for Content Provenance and Authenticity (C2PA) specification is an open technical standard for a tamper-evident attribution schema. It works by:

• Creating a cryptographically sealed manifest that is bound to a content asset.
• The manifest records the assertions made by each actor in the content's lifecycle—the photographer, the editor, the publisher.
• This creates a verifiable attribution chain from the initial capture device to the final published file, allowing a generative AI system to cryptographically validate the entire provenance graph before using the asset as a source.

For datasets, the W3C's Data Catalog Vocabulary (DCAT) is the standard schema for attribution. It enables publishers to describe:

• The dcat:Dataset itself, with its title, description, and release date.
• The dct:creator and dct:publisher to establish organizational provenance.
• The dct:license to define usage rights.
• A model searching for a training data source can parse a DCAT catalog to instantly understand a dataset's origin, governance, and legal constraints without manual review.