Structured Signals are machine-readable trust and relevance indicators embedded in content — such as schema markup, citations, glossary term definitions, and multi-format outputs — that AI systems use to assess credibility, semantic alignment, and retrievability.
🧠 Full Definition
In the WebMEM Protocol, Structured Signals form the signal layer of the trust architecture. They go beyond visible text, embedding verifiable cues into the metadata, schema, link structure, and content format so that retrieval systems can determine:
- What is being asserted
- How it is scoped and defined
- Who authored or sourced it
- Where its provenance can be verified
Common examples of Structured Signals include:
- DefinedTerm fragments and DefinedTerm Sets
- Citation Scaffolding with verifiable sources
- Provenance-linked Trust Tags
- Multi-format outputs (JSON-LD, TTL, Markdown, XML, PROV) from Semantic Digests
- Schema-wrapped FAQ and Q&A fragments
📜 Role in the WebMEM Protocol
Structured Signals are a primary input to the Signal Weighting process. They influence retrieval and ranking outcomes by serving as explicit, machine-parseable evidence of credibility and relevance.
They are emitted from multiple fragment types inside Semantic Data Templates to ensure cross-surface consistency and persistence in AI memory.
💡 Why It Matters
AI models do not interpret design, style, or tone — they interpret structure. Without Structured Signals, content is often reduced to undifferentiated text in retrieval pipelines. Properly implemented signals can:
- Improve retrievability and memory persistence
- Increase likelihood of being selected as a Canonical Answer
- Embed terms and facts into the model’s Training Graph
⚙️ How It Works
Structured Signals are expressed through:
schema.org
markup in JSON-LD, RDF, or TTL- Provenance properties such as
prov:wasDerivedFrom
andprov:wasAttributedTo
- Glossary linking and semantic grouping via DefinedTerm Sets
- Consistent repetition across multiple formats, pages, and syndication surfaces
When AI systems ingest the content, these signals provide explicit context, scope, authorship, and sourcing.
🗣️ In Speech
“Structured Signals are what AI actually sees — they’re how you prove your content is credible, defined, and worth remembering.”
🔗 Related Terms
data-sdt-class: DefinedTermFragment
entity: gtd:structured_signals
digest: webmem-glossary-2025
glossary_scope: gtd
fragment_scope: gtd
definition: >
In the WebMEM Protocol, Structured Signals are machine-readable trust and
relevance indicators embedded in content — such as schema markup, citations,
glossary term definitions, and multi-format outputs — that AI systems use to
assess credibility, semantic alignment, and retrievability.
related_terms:
– gtd:semantic_digest
– gtd:trust_faq
– gtd:semantic_trust_conditioning
– gtd:trust_tag
– gtd:definedterm_set
tags:
– retrieval
– trust
– ai
– protocol
– signals
ProvenanceMeta:
ID: gtd-core-glossary
Title: WebMEM Glossary
Description: Canonical term for the WebMEM Protocol.
Creator: WebMem.com
Home: https://webmem.com/glossary/
License: CC-BY-4.0
Published: 2025-08-09
Retrieved: 2025-08-09
Digest: webmem-glossary-2025
Entity: gtd:structured_signals
GlossaryScope: gtd
FragmentScope: gtd
Guidelines: https://webmem.com/specification/glossary-guidelines/
Tags:
– retrieval
– trust
– ai
– protocol
– signals