How to Structure your Information for Different Purposes, AI Environments, and Human Audiences
The AI search identity trap stems from a fundamental misunderstanding: AI does not search for raw keywords.
AI engines search for context, relationships, and meaning. Organizations fail when they try to feed AI through the same basic structures used for traditional, keyword-driven portals.
Both Google’s public Knowledge Graph and Google Cloud’s Knowledge Catalog (formerly part of Dataplex) utilize graph-based data structuring to manage information. However, they serve fundamentally different purposes. Vital clarity between the two and the Open Knowledge Format is neeed and drives the purpose of this article.
In peer and client conversations, I find this is a major blind spot. Yet, it represents a larger, systemic set of misunderstandings that search engine optimizers (SEOs) and digital marketers make. Here are highly level definitions.
- Google’s public Knowledge Graph: is for public, entity-level information retrieval.
- Google Cloud’s Knowledge Catalog: is an always-on context engine for internal, enterprise data governance. It unifies structured, unstructured, and SaaS data into a governed, agent-ready truth for trusted AI.
- The Open Knowledge Format (OKF): represents a standardized way for agents to access and process knowledge via very simple directories of Markdown files.
Your data catalog tells AI agents what data exists and where it resides. Your knowledge graph tells AI what entities mean and how they relate.
A fascinating alignment in engineering philosophy exists. Understanding their differences opens up massive opportunities.
The Knowledge Catalog nwas formerly known as Dataplex. It operates inside a company’s private cloud estate, harvesting metadata from internal databases (like BigQuery, Spanner, and GCS) and private software (like SAP, Salesforce, and Workday). Its goal is to keep internal company AI agents from hallucinating on private corporate data.
The public Knowledge Graph’s common tool are schema markup, Wikidata, and Wikipedia. The goal is to seed external, open-source signals so that Google’s core public-facing algorithms confidently index you in the public Knowledge Graph and recommend you in consumer search (AI Overviews, Perplexity, ChatGPT Search)
A practitioner might mistakenly read the Google Cloud announcement and think they can buy or use the GCP Knowledge Catalog to force their business into Google’s public search Knowledge Graph.
Distinguish Between Google’s public Knowledge Graph and Google Cloud’s Private Knowledge Catalog
| Comparison Dimension | Google’s Public Knowledge Graph (Entity Engineering) | Google Cloud Knowledge Catalog (Enterprise Data Systems) |
|---|---|---|
| Core Definition & Purpose | Google’s global, public database that maps real-world entities (people, places, organizations) and their relationships. It is designed to understand “things, not strings” to answer public search queries and fuel organic recommendations. | A dynamic, always-on context engine (representing the evolution of Google Cloud’s Dataplex) designed to unify corporate metadata, enrich it using AI, and supply private LLM agents with reliable enterprise context [5, 6]. |
| Primary Users & Target Audience | Chief Marketing Officers (CMOs), digital marketing agencies, SMB business owners, and individuals seeking to establish or repair their public brand identity and personal author authority. | Enterprise developers, cloud architects, IT leaders, and internal business stakeholders building secure corporate AI agents. |
| Data Scope & Privacy Boundaries | Entirely public-facing and open-web based. It is fed by open registries, public reference sites, and crawler-accessible web properties. | Strictly private, governed corporate cloud estates. It indexes internal corporate data across databases, object storage, and business applications. |
| Primary Inputs & Structural Signals | Public structured schema.org markup (JSON-LD), Wikidata records, Wikipedia pages, NAP (Name, Address, Phone) consistency across local business directories, and public co-citation networks. | SQL schemas, semantic models (LookML), BigQuery measures, pre-packaged corporate “Data Products” (with built-in SLAs and intent), and raw unstructured files. |
| AI Engine & Integration Layer | Feeds Google’s public Retrieval-Augmented Generation (RAG) search pipeline (AI Overviews) and query fan-out mechanisms to serve consumer search engines. | Feeds internal, enterprise-scoped AI solutions, such as the Gemini Enterprise Deep Research Agent, to allow corporate tools to reason over private datasets. |
| Access Control & Security | Publicly accessible by design. No internal permissions apply; the goal is maximizing public machine legibility [4, 19]. | Strictly access-control-aware. Search and retrieval systems respect source metadata permissions to guarantee agents only access files they are authorized to see. |
| Governance & Method of Entry | Publishers have no direct control or manual input. Entry is earned indirectly by verifying trust signals over time; “graph entry is the last thing to arrive.” | Fully curated, built, and automated by internal engineering team. Uses tools like deep multimodal Gemini metadata extraction to auto-build entity pipelines from raw data buckets. |
| Consequences of Gaps / Failures | Leads to “AI invisibility” where the brand is omitted from public AI search recommendations, or identity hybridization errors where unstructured profiles are merged with other people. | Causes internal AI agent hallucinations, high search latency, broken SQL query joins, and stale or untrusted corporate insights. |
Knowledge Mapping
Knowledge management (KM) in 2026 is a strategic asset driven by enterprise AI and knowledge mapping.
When structured data isn’t factored in, AI systems are forced to rely on “inference,” which is highly error-prone and computationally expensive. Google’s blog validates this verbatim, warns that when AI agents lack explicit semantics, “this triggers hallucinations, high latency, and stale insights.”
The AI Tech Giant implores developers to: “Stop forcing your agents to guess the unwritten rules of your business. Build the context once…” See the Always-on context and governance for your agents article, published on April 22, 2026.
Google’s and Bing’s dedicated knowledge graph pipelines, but it remains largely invisible to newer, LLM-based flat-text systems. OKF elegantly solves this architectural split. By standardizing entity context as markdown files containing YAML frontmatter, it feeds both search architectures simultaneously.
I wrote in September 2024 on further distinguishing between the Knowledge Graph vs. Knowledge Panel vs. Google Business Listing.
Google’s New Open Knowledge Format (OKF) Helps Agents Acessing Knowledge
The introduction of Google’s OKF represents a massive tectonic shift in the semantic web and AI search landscapes. It directly bridges the gap between public entity optimization and private corporate data systems
I see OKF as a way to give AI agents the precise context they need to do their jobs without locking your data into a proprietary platform or complex databaseIt works via very simple directories of Markdown files.
The Core data knowledge problem OKF solves:
- In any system or organization, critical knowledge is highly fragmented.
- It lives in database schemas, code comments, API documentation, internal wikis, and the heads of individual developers.
- When you build an AI agent (like a coding assistant or a data analyst) and ask it to perform a task, it has to assemble this context from scattered, mutually incompatible systems. Usually, developers try to solve this by building complex integrations or custom APIs.
- OKF’s philosophy is that the solution isn’t another software service—it’s a standardized file format. It formalizes the “LLM-wiki” pattern: a simple folder of markdown files that both humans can easily read and edit, and AI agents can seamlessly parse and write to
The Data Knowledge Pipeline: The Role of OKF
When I consider the Open Knowledge Format (OKF) side-by-side with Google’s public Knowledge Graph and Google Cloud’s private Knowledge Catalog, you can see how OKF acts as a conceptual bridge.
This understanding helps when using AI and Knowledge Graphs for better content and context.
While the public Knowledge Graph is about public brand authority and the Cloud Knowledge Catalog is about governed enterprise search, OKF is the open-standard data format that makes knowledge exchange between humans, databases, and AI agents painless. entity search
| Comparison Dimension | Google’s Public Knowledge Graph (Entity Engineering) | Google Cloud Knowledge Catalog (Enterprise Data Systems) | Open Knowledge Format / OKF (Agent-Oriented Metadata) |
|---|---|---|---|
| Core Definition & Purpose | Google’s global, public database that maps real-world entities (people, places, organizations) and their relationships. It is designed to understand “things, not strings” to answer public search queries and fuel organic recommendations. | A dynamic, always-on context engine (representing the evolution of Google Cloud’s Dataplex) designed to unify corporate metadata, enrich it using AI, and supply private LLM agents with reliable enterprise context. | An open, vendor-neutral specification that formalizes the “LLM-wiki” pattern into a portable, interoperable format. It standardizes the metadata, context, and curated knowledge that modern AI systems need. |
| Primary Users & Target Audience | Chief Marketing Officers (CMOs), digital marketing agencies, SMB business owners, and individuals seeking to establish or repair their public brand identity and personal author authority. | Enterprise developers, cloud architects, IT leaders, and internal business stakeholders building secure corporate AI agents. | Developer teams, agent builders, and data teams seeking to prevent redundant effort when feeding context to multiple different AI agents. |
| Data Scope & Privacy Boundaries | Entirely public-facing and open-web based. It is fed by open registries, public reference sites, and crawler-accessible web properties. | Strictly private, governed corporate cloud estates. It indexes internal corporate data across databases, object storage, and business applications. | System-agnostic. It runs locally on any filesystem, inside a Git repository, or packed as a simple .tar file. It can represent either public or private concepts. |
| Primary Inputs & Structural Signals | Public structured schema.org markup (JSON-LD), Wikidata records, Wikipedia pages, NAP (Name, Address, Phone) consistency across local business directories, and public co-citation networks. | SQL schemas, semantic models (LookML), BigQuery measures, pre-packaged corporate “Data Products” (with built-in SLAs and intent), and raw unstructured files. | Markdown “concept” files representing assets (tables, metrics, APIs) structured with YAML frontmatter (type, title, description, resource, tags, timestamp) and connected via relative markdown links. |
| AI Engine & Integration Layer | Feeds Google’s public Retrieval-Augmented Generation (RAG) search pipeline (AI Overviews) and query fan-out mechanisms to serve consumer search engines. | Feeds internal, enterprise-scoped AI solutions, such as the Gemini Enterprise Deep Research Agent, to allow corporate tools to reason over private datasets. | Bridges the divide by serving structured, queryable key-value pairs (via YAML) to databases while presenting clean, chunkable prose (via Markdown) to LLM RAG pipelines. |
| Access Control & Security | Publicly accessible by design. No internal permissions apply; the goal is maximizing public machine legibility. | Strictly access-control-aware. Search and retrieval systems respect source metadata permissions to guarantee agents only access files they are authorized to see. | Inherited from the host platform. The format is format-only; security is managed by whichever repository, cloud storage bucket, or platform (like GCP) hosts the files. |
| Governance & Method of Entry | Publishers have no direct control or manual input. Entry is earned indirectly by verifying trust signals over time; “graph entry is the last thing to arrive”. | Fully curated, built, and automated by internal engineering teams. Uses tools like deep multimodal Gemini metadata extraction to auto-build entity pipelines from raw data buckets. | Completely self-directed. Files can be hand-authored by a human, exported automatically by a database script, or autonomously written and updated by AI agents. |
| Consequences of Gaps / Failures | Leads to “AI invisibility” where the brand is omitted from public AI search recommendations, or identity hybridization errors where unstructured profiles are merged with other people. | Causes internal AI agent hallucinations, high search latency, broken SQL query joins, and stale or untrusted corporate insights. | Results in siloed, fragmented “context deserts” where every agent developer must solve the context-assembly problem from scratch using bespoke formats. |
Data Ingestion vs. Data Presentation: Why OKF is Not a Claude Artifact
OKF when creating knowledge artifacts
I’m stunned at the pace of our rapidly evolving AI landscape. Confusion commonly arises between Frontend UI Features and Backend Data Standards.
I frequently see digital marketers generating a Markdown document in a Claude Artifact and mistakenly assuming they are “building a knowledge graph.” This is a fundamental misunderstanding of how data flows in the agentic web.
To maximize the value of your AI architecture, it helps to identify past and emerging types of data knowledge.
Distinguish between how AI outputs data versus how it ingests data:
- Claude Artifacts (The Output): Artifacts are a user interface feature built into Anthropic’s Claude. They allow the AI to generate, render, and display self-contained content—like Markdown documents, React web apps, or SVG graphics—in a dedicated window next to your chat.
Artifacts are temporary workspaces designed for humans to visually interact with the results of an AI’s reasoning. They are bound by chat session limits and restricted to megabytes of data.
- COpen Knowledge Format (The Input):e OKF is a vendor-neutral file format specification (combining Markdown with YAML frontmatter). It is a permanent, version-controlled architecture designed to structure your source knowledge.
Whether you are using Google’s Gemini, Anthropic’s Claude, or a custom internal agent, OKF ensures the AI can accurately crawl, query, and understand the internal logic of your business at enterprise scale.
Understanding the New Knowledge, Data, and AI Ecosystem in AI Architecture
The synergy between OKF and AI workspaces
These two concepts don’t compete; they collaborate perfectly. OKF acts as your business’s active “LLM-Wiki.” When you structure your corporate data using OKF, you create a pristine, machine-readable repository.
If you upload that OKF directory into an enterprise AI tool, the agent reads that explicit context to perform its tasks. The AI can then use a UI feature like a Claude Artifact to present its findings, generate a product requirements document, or visualize your data without hallucinating.
The bottom line: You build an OKF architecture to feed the AI reliable facts; the AI uses tools like Artifacts to show you its work.
Gemini Enterprise & The Deep Research Agent
Sitting on top of these retrieval layers are Google’s actual reasoning systems, such as Gemini Enterprise.
- The Deep Research Agent in Gemini Enterprise, which is a native agentic system powered by the Knowledge Catalog.
- It retrieves data across private documents, databases, and the public web, synthesizing these separate knowledge layers to answer complex multi-step questions with citations.