Definition and structure
A topical map organises a subject area hierarchically: the main topic as the top-level entity, below it cluster topics (usually five to twelve per main topic), sub-clusters and support topics — connected into an entity graph aligned with Wikidata and industry knowledge bases. The result is typically a map of 80 to 300 nodes that makes the content inventory, the gaps and the relationships visible.
Creation: four sources
- Wikidata SPARQL: the canonical entity hierarchy of the field.
- SERP extraction: real intent clusters from search results, People-Also-Ask and related searches.
- Competitor delta: topics competitors cover that your domain does not.
- LLM expansion: semantically adjacent entities and questions keyword tools do not surface.
Mapping tools provide starting hypotheses — the critical work is the manual verification of relevance and priority. An unverified tool map produces content plans with lots of effort and little effect.
Steering function
- Production order: clusters are built out completely instead of scattering single articles — completeness is the signal for topical authority.
- Internal linking: the map's relationships define the link architecture (hub-and-spoke or mesh instead of rigid silos).
- Fan-out coverage: generative systems decompose queries into sub-queries (query fan-out) — the map shows whether those sub-query spaces are covered.
- Iteration: the map is not a one-off deliverable; every new content piece and every SERP shift extends it.
Related terms
The topical map is the operational tool for topical authority in semantic search. The full hands-on workflow is in the article Topical maps & content architecture.
The map steers, not the keyword list
Semantic search and fan-out retrieval reward complete topic coverage. The topical map makes completeness plannable — and gaps visible before competitors fill them.
FAQ on topical maps
How does a topical map differ from a keyword list? ▾
A keyword list sorts search terms by volume. A topical map organises a subject area as an entity graph with hierarchy and relationships — it plans completeness and linking instead of single hits, making it the right tool for semantic search and generative retrieval.
How big should a topical map be? ▾
Typically 80 to 300 nodes per main topic — depending on competitive density and business relevance. What matters is not size but prioritisation: clusters with commercial weight and a realistic authority chance come first.
What role does the map play for generative visibility? ▾
Generative systems decompose queries into sub-queries and synthesise from several retrieval runs. If you only cover the main query, you are absent from the sub-answers. The map plans exactly this sub-query coverage systematically.
Are mapping tools enough to build one? ▾
As a starting point yes, as a result no. Tools deliver hypotheses from SERP and corpus data; verifying relevance, redundancy and priority remains manual expert work — otherwise you get a busywork plan without effect.