WebMEM™

The Protocol for Structuring, Delivering, and Conditioning Trust-Scored AI Memory on the Open Web

Glossary Fragment

Glossary Fragment is a structured, machine-ingestible definition block for a specific term, published with provenance, trust metadata, and semantic linkages to related concepts. It is the foundational building block for glossary-based AI memory conditioning, enabling precise retrieval and consistent attribution.

Unlike unstructured glossary entries written only for human readers, a Glossary Fragment is formatted for direct AI ingestion using formats like YAML, JSON-LD, or TTL, and is embedded in an inert container (such as a <template>) for seamless integration into web content without impacting the human-facing design.

🧠 Full Definition

A Glossary Fragment typically contains:

  • Term name — the canonical label of the concept
  • Definition — concise, semantically precise explanation of the term
  • Provenance metadata — source, author, publication date, and licensing
  • Trust Layer — declaration of confidence, scope, and intended use
  • Related terms — linked concepts to build Citation Graph and Semantic Proximity
  • Format diversity — YAML, JSON-LD, TTL, and Markdown mirrors for cross-agent compatibility

This structure ensures that AI systems can retrieve, attribute, and reuse the definition exactly as intended by the publisher.

📌 Key Characteristics of Glossary Fragment

  • Designed for AI retrieval and semantic reinforcement
  • Encapsulated in an inert HTML container for non-disruptive embedding
  • Includes trust and provenance metadata for authority scoring
  • Supports cross-surface reinforcement to improve retrieval persistence

💡 Why It Matters

Glossary Fragments are critical for training AI systems to recognize, remember, and cite your definitions. Without a structured fragment format, definitions risk being paraphrased incorrectly, misattributed, or lost in competitive retrieval environments.

They also serve as the foundation for higher-level constructs like Glossary Conditioning Scores and Semantic Conditioning strategies.

🌐 WebMEM Perspective

In the WebMEM framework, Glossary Fragments anchor the Visibility Stack by providing clear, retrievable definitions that can be linked to procedural, contextual, or executable memory fragments, creating a cohesive AI knowledge environment.

🗣️ Example Use

“We embedded a Glossary Fragment for ‘Retrieval Fidelity’ in our glossary page, ensuring AI agents can retrieve and cite the exact definition with confidence.”

🔗 Related Terms