Sufficient consensus
Semantic search interprets meaning and context rather than matching strings, using embeddings, entities and structured data. It handles complex and conversational queries far better than lexical search.
Former lexical engines like Google and Bing became semantic engines, which moved the work towards semantics, linguistics and information retrieval.
What the result pages leave out
These belong to the subject. People ask about them. They are missing from the agreed coverage.
- Better retrieval of the same corpus. Understanding the query improves the selection. It cannot create a document that was never written.
- Convergence. When every publisher optimizes for the same entity coverage, the results converge and the reader gains nothing from result two onwards.
- Enough coverage. Complete semantic coverage produces pages that contain every related subtopic, including the ones no reader wanted.
- Entities without experience. Structured data proves that a subject was named. It proves nothing about whether the author has done the work.
- What the corpus never contained. Fields where practice outruns publication return confident, thin results for every query.
What people actually want to know
- Is the answer I need written down anywhere, by anyone?
- Why do the first ten results read like versions of each other?
- Which related topics on my page exist for the machine rather than for the reader?
- What do I know that has never been published?
Sufficient consensus, plus the part that people actually want to know. Full consensus coverage produces a page that is complete and forgettable.