A network of entities and relationships used by search engines to represent real-world knowledge.
Knowledge graphs organize entities (people, places, concepts) and their relationships, enabling semantic understanding and richer answers. Aligning content with recognized entities and structured data helps search engines connect pages to the broader graph, improving disambiguation and eligibility for enhanced results.
| Criteria | Recommended | Notes |
|---|---|---|
| Intent Alignment | Map $knowledge graph to user journeys and topics | Validate with SERP and on-site behavior |
| Consistency | Encode in components and content standards | Use CMS validations and preview QA |
| Measurement | Track internal CTR, sync signals, conversions | Set guardrails to catch regressions |
Clarifying an entity with schema.org, IDs, and consistent naming
Interlinking related entities within topic clusters
Improves semantic relevance and discovery
Enables richer SERP features and connections
Supports scalable internal linking by entity
Use precise, unambiguous entity names
Provide identifiers (sameAs, IDs) where possible
Model relationships with structured data
Search technology that understands the contextual meaning and intent behind queries.
Mathematical representations of text that capture semantic meaning in multi-dimensional space.
Optimizing content around real-world entities and their relationships, not just keywords.
Structured data that helps search engines understand content and enable rich results.
The mix of anchor text types used across a site’s internal and external links.
Links that connect one page of a website to another page on the same domain.
User experience metrics focused on loading, interactivity, and visual stability.
RankVectors uses AI-powered semantic analysis to help you implement intelligent internal linking strategies automatically.
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