Knowledge Base & Content Management

Structured Data

Definition

Structured data is information that conforms to a rigid schema: each field has a defined name, type, and meaning. Examples include: product pricing tables (plan name, price, features), compatibility matrices (feature X supported on platform Y: true/false), and FAQ entries with explicit question and answer fields. Structured data is highly queryable — you can ask 'which plans include the API feature?' against a structured pricing table and get a precise answer. In knowledge base systems, structured data is often stored alongside unstructured text, with the retrieval system able to query both.

Why It Matters

Structured data enables a category of precise, factual answers that unstructured text often cannot deliver reliably. Questions like 'what is the price of the Enterprise plan?' are answered most accurately from a structured pricing table, not from an article where the price might be mentioned in passing and may not be updated when pricing changes. Storing key facts as structured data reduces the risk of outdated or inconsistent AI answers for commonly asked factual questions.

How It Works

Structured data is ingested into the knowledge base using schema-aware parsers that map fields to defined types. It is stored in a relational or document database and indexed for exact-match and range queries. At retrieval time, the AI system can query structured data directly using generated SQL or JSON queries, or it can be serialized to text and included in the AI's context. Hybrid approaches use structured data for facts and unstructured text for explanations.

Structured Data — Schema to Rich Snippet

JSON-LD Schema

{
"@type": "FAQPage",
"mainEntity": [{
"@type": "Question",
"name": "How do I reset..."
}]
}

Search Result Rich Snippet

Help Center — Frequently Asked Questions

docs.example.com/faq

+ How do I reset my password?
+ How do I cancel my plan?

Schema markup helps search engines display richer, more clickable results

Real-World Example

A chatbot for a SaaS product answers pricing questions from a structured pricing table: {plan: 'Pro', price: '$49/month', api_access: true, seats: 5}. When a user asks 'Does the Pro plan include API access?', the system queries the structured table and returns 'Yes, the Pro plan includes API access.' The answer is always accurate because it comes from the source-of-truth structured data, not from a text article that might be out of date.

Common Mistakes

  • Storing structured data only in unstructured text articles, making factual updates tedious and creating inconsistency risks.
  • Not defining a schema for structured data, making it unmaintainable as the knowledge base grows.
  • Forgetting to update structured data when product details change — outdated structured facts are worse than no structure because they are retrieved with high confidence.

Related Terms

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