The KSM™ Framework
One model for whether AI can understand, recognize, and cite you.
KSM™ is a socio-technical framework created by Dr. Anthony Q. Bowen that treats AI visibility as the product of three interdependent pillars rather than a list of tactics.
AI Visibility = f(E × S × C)
Extractable Structured Content · Entity Salience · Citation Authority
Three pillars · one system
The pillars multiply. The weakest one governs the outcome.
Because the pillars are multiplicative, strong content cannot compensate for an ambiguous entity, and a well-modeled entity cannot compensate for absent independent corroboration.

- 01E · 40%
Extractable Structured Content
Can AI understand you?
Measures how clearly machines can access, parse, and extract your knowledge from page structure, schema, metadata, FAQs, evidence blocks, and crawlable content.
- 02S · 40%
Entity Salience and Knowledge Graph Alignment
Can AI recognize you?
Measures whether your brand, people, products, research, and identifiers form a consistent, connected, and unambiguous entity across your site and authoritative external sources.
- 03C · 20%
Citation Authority Reinforcement
Can AI trust and cite you?
Measures whether independent publications, scholarly sources, media, industry references, and third-party applications reinforce your claims and authority.
The pillars multiply rather than add: a weak pillar constrains the entire score. Weights are fixed at 40 / 40 / 20 across every KSM™ assessment.
Plain language
KSM™ Explained Simply
Three questions, asked in order. If AI cannot understand your knowledge, recognition never happens. If it cannot recognize your entity, citation never happens.

Signals
What each pillar examines.
E · 40%
Extractable Structured Content
Can AI understand you?
Measures how clearly machines can access, parse, and extract your knowledge from page structure, schema, metadata, FAQs, evidence blocks, and crawlable content.
- Machine-readable page structure and heading hierarchy
- Valid, linked JSON-LD that matches visible content
- Evidence blocks, definitions, and answerable FAQ content
- Crawlable, server-rendered critical copy
S · 40%
Entity Salience and Knowledge Graph Alignment
Can AI recognize you?
Measures whether your brand, people, products, research, and identifiers form a consistent, connected, and unambiguous entity across your site and authoritative external sources.
- Consistent organization, people, and product naming
- Stable entity identifiers reused across properties
- Knowledge-graph alignment with authoritative external sources
- Disambiguation from similarly named entities
C · 20%
Citation Authority Reinforcement
Can AI trust and cite you?
Measures whether independent publications, scholarly sources, media, industry references, and third-party applications reinforce your claims and authority.
- Independent publications and media references
- Scholarly and research citations
- Industry references and third-party applications
- Corroboration of claims outside your own domain
Framework identity
A citable framework with a stable identifier.
The framework is published, versioned, and citable. It is identified as https://ksmmodel.ai/#ksm-framework and is creator-linked to Dr. Bowen's Person entity at https://dranthonyqbowen.com/#person.
Bowen, A. Q. (2026). The Knowledge Structuring Model (KSM™): A Socio-Technical Framework for AI-Mediated Visibility and Citation Authority. SSRN Working Paper 6721140.
