How to build a topical map that mirrors how search engines model a subject

How to build a topical map that mirrors how search engines model a subject

Most topical maps are just keyword lists with headers. Here's a procedure grounded in entity relationships instead.

— Step 1: pull the Wikipedia article for your core topic. Copy every internal link in the first three sections — those are the entities the topic is co-defined by.
— Step 2: query Google's Knowledge Graph Search API for your core entity; record the connected types it returns.
— Step 3: group the resulting entities into 'is-a', 'part-of', and 'used-for' relationships. These three relation types cover most informational intent.
— Step 4: assign one URL per cluster, not per keyword. A cluster is a set of entities that share a relation.
— Step 5: check coverage by scraping the 'People Also Ask' tree three levels deep; any branch with no matching URL is a gap.

Method note: built from Wikipedia link graphs plus the public Knowledge Graph API across 12 test topics.

Caveat: Wikipedia over-represents notable entities and under-represents commercial ones, so supplement with SERP scraping for transactional subjects.

Confidence: medium