Structured data, JSON-LD and discoverability
Understand relationships between semantics, metadata and AI systems.
Linked data on the Web
JSON-LD represents resources and relations in JSON with a context defining identifiers. Linked data is neither an agent instruction nor a promise that a model will reproduce the content.
Schema.org and search
Page descriptions can clarify resource type, author, date or relationships. Google requires structured data to match visible content; artificial or misleading markup does not necessarily improve search eligibility.
Interoperability and limits
Stable identifiers and defined types facilitate exchange across systems, but graphs require maintenance. Mistyped or context-free elements can introduce ambiguities comparable to natural language.
Hands-on exercise
Represent a course page with title, author, date, language and references, then verify that those facts really appear on the page. Compare that model with text without metadata.
Useful metadata, not magic
JSON-LD and Schema.org can clarify resource types, authors, dates and relationships. They do not force any agent or search service to use these claims. Values should match real visible information. Stable URIs help connect resources, but identifiers do not prove semantic relationships.
Exercise: interoperable course record
Describe a course with title, language, documented author and URL. Publish consistent visible and structured information, then validate syntax and meaning. Deliberately alter a date in JSON-LD and ask learners to identify the inconsistency. Good practice includes maintaining both representations together.
Core steps
- 01Choose stable identifiers
- 02Define JSON-LD context
- 03Check visible content consistency
- 04Test the graph
Three questions to ask
Does the markup match the visible page?
Is it a search ranking guarantee?
Are identifiers stable and meaningful?
Read the source documents
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