Semantic Search & Post-Cookie Optimization Graphics
Content Originality and Ingestion Math
Search systems increasingly reward information gain and penalize near-duplicates. Ingestion math is how you measure uniqueness, coverage, and overlap across a corpus before it ships. Treating originality as a number, not a feeling, keeps large content programs from quietly flooding the index with sameness.
要点
- Measure similarity between new pages and existing ones before publishing.
- Track unique facts, examples, and entities per page, not just word count.
- Set an overlap threshold and block or merge pages that exceed it.
- Compare against top-ranking competitors to ensure real information gain.
- Deduplicate at ingestion time, not after indexation.
- Keep a canonical registry so near-duplicates route to one URL.
- Review low-originality pages for refresh or consolidation.
- Report originality metrics alongside traffic to prove value.
よくある質問
What is information gain in this context?
How much new value a page adds beyond what already exists in the index.
Can I measure originality automatically?
Yes with similarity and overlap metrics, but human review still matters.
Does AI-generated content fail originality?
Not if it adds new facts and structure; sameness is the risk, not the tool.
How do I fix low-originality pages?
Add original data, examples, and perspective, or merge them into a stronger page.
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