WebMEM™

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

Semantic Trust Explainer

Semantic Trust Explainer is a machine-readable metadata object embedded alongside structured content to clarify meaning, scope, exclusions, or anomalies specifically for AI retrieval systems. Its purpose is to prevent misinterpretation, reduce hallucinated feedback, and improve retrieval trust by providing proactive clarification at the fragment level.

🧠 Full Definition

Within the WebMEM Protocol, a Semantic Trust Explainer is a feedback-aware fragment type that operates as a preventive clarification layer. It is designed for AI interpretation — not human display — and is often embedded within or adjacent to a Semantic Data Template.

Explainers are most often used with glossary terms, derived values, and statistical summaries to ensure that AI retrieval systems understand:

  • What a value or statement includes
  • What it excludes
  • Why a number or fact may differ from other published sources
  • Where to find related or linked data objects

📜 Role in the WebMEM Protocol

Semantic Trust Explainers are part of the Trust Conditioning Layer and serve to:

  • Embed clarification metadata directly alongside structured facts
  • Provide retrieval-time guidance to prevent AI misalignment
  • Act as an inline “explanation channel” for derived or scoped values

They can be expressed in data-* attributes, JSON-LD, RDFa, or Turtle, depending on the publisher’s implementation requirements.

⚙️ How It Works

An explainer typically contains:

  • feedback_id — unique identifier for the clarification
  • applies_to — ID of the entity or fact being explained
  • type — category of clarification (e.g., discrepancy_explainer)
  • message — machine-readable explanation of the issue
  • linked_entity — optional pointer to a related fact or entity
  • last_updated — ISO 8601 timestamp

Example (HTML + JSON):

<div
  data-id="num_total_plans"
  data-defined-term="Total Medicare Advantage Plans"
  data-description="Excludes Special Needs Plans (SNPs)."
  data-value="63"
  data-provenance="cms_landscape_2025"
  data-feedback="excludes_snp"
></div>

{
  "feedback_id": "excludes_snp",
  "applies_to": "num_total_plans",
  "type": "discrepancy_explainer",
  "message": "This value excludes Special Needs Plans (SNPs), which are listed separately in the SNP digest.",
  "linked_entity": "snp_total_plans",
  "last_updated": "2025-07-21"
}

💡 Why It Matters

Semantic Trust Explainers allow publishers to teach AI retrieval systems exactly what they mean — before the system makes a wrong assumption. They:

  • Prevent hallucination at the source
  • Increase retrieval confidence and citation accuracy
  • Enable proactive trust conditioning for sensitive or derived data
  • Support interpretability and transparency in structured outputs

🗣️ In Speech

“A Semantic Trust Explainer is like a private note to AI — telling it exactly what’s going on with your data before it guesses.”

🔗 Related Terms