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Schema.org is a reference website that publishes documentation and guidelines for using structured data mark-up on web-pages (in the form of microdata, RDFa or JSON-LD). Its main objective is to standardize HTML tags to be used by webmasters for creating rich results (displayed as visual data or infographic tables on search engine results) about a…
The analysis highlights History and Standards as prominent areas in the source structure around Schema.org.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around Schema.org shows recurring relationship patterns in the source. For example, Schema.org → Bing, FOAF, GoodRelations, Google, In, In November, JSON-LD, June, Microdata, Microformats, Much, OpenCyc, Public, RDFa, Russia, Schema, Semantic Web, September, Such, The Another extracted example is Schema.org → CSV, GitHub, Google, In June, It, JSON, Schema, This, To. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
schema org microdata using data web google json-ld search rdfa website yandex structured used types initiative usage statistics semantic schemas
TTTA extracted 61 structured relationships around Schema.org. Examples in this analysis include Schema.org → Abbreviation → schema and Schema.org → Base standards → URI, HTML5, RDF, Microdata, ISO 8601. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Schema.org | Abbreviation | schema | 1.00 | infobox |
| Schema.org | Base standards | URI, HTML5, RDF, Microdata, ISO 8601 | 1.00 | infobox |
| Schema.org | Domain | Semantic Web | 1.00 | infobox |
| Schema.org | Latest version | 30.0; 25 March 2026 | 1.00 | infobox |
| Schema.org | License | CC-BY-SA 3.0 | 1.00 | infobox |
| Schema.org | Organization | Google, Yahoo!, Microsoft, Yandex | 1.00 | infobox |
| Schema.org | Related standards | RDFa, Microformat, RDFS, OWL, N-Triples, Turtle, JSON, JSON-LD, CSV | 1.00 | infobox |
| Schema.org | Website | schema.org | 1.00 | infobox |
| Schema.org | Year started | 2011; 15 years ago (2011) | 1.00 | infobox |
| Schema.org | is a | reference website that publishes documentation and guidelines for using structured data mark-up on web-pages | 0.90 | text |
| Organization | instance of | Some schema markups | 0.80 | text |
| Person are commonly used to influence search results returned by Google's Knowledge Graph.The popularity of Schema.org has served for its use as a base for other schemas | instance of | Some schema markups | 0.80 | text |
The concept neighborhoods around Schema.org bring nearby vocabulary together. In this analysis, examples include Schema, Microdata and Using. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Schema.org, one of the stronger structural bridges in this analysis connects Schema.org with History. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Schema.org to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History & Standards, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Schema.org · EN edition · Analysis: TopicsToTalkAbout