Entity-based search spans from simple conceptual ideas to deeply technical patent analysis. Find your entry point and move at your own pace.
No prior SEO knowledge needed. These articles explain core concepts from scratch, using plain language and concrete examples.
In Google's vocabulary, an entity is any thing that can be distinctly identified. A person, a place, a business, a concept. This article explains what entities are, how they differ from keywords, and why the distinction matters for search.
A beginner's introduction to what the Knowledge Graph is, where it came from, and what it's used for. Covers the acquisition of Metaweb in 2010, the introduction of the Knowledge Graph in 2012, and how it's grown since.
A local business owner's introduction to entity-based search. No technical jargon. Explains the practical difference between Google knowing your business exists and Google understanding what your business is.
Schema markup explained for non-developers. What it is, why it exists, and what it communicates to Google. Uses the analogy of a business card versus a full company profile to illustrate the difference structured data makes.
For readers who understand basic SEO concepts and want to go deeper into how entity signals actually work. Some technical vocabulary used.
Google officially recommends JSON-LD for structured data. This article explains the technical reasons behind that recommendation, how each format interacts with the DOM, and what the processing difference means for entity signal reliability.
Wikipedia's notability guidelines and Google's entity confidence thresholds have meaningful overlap. This article compares the two systems, explains where they align and where they diverge, and discusses what that means for businesses seeking Wikipedia coverage.
Google's local pack (the map results) appears to be influenced by entity confidence, not just proximity and relevance. This article examines what published research and patent descriptions suggest about how entity clarity affects local pack inclusion.
Primary source analysis. Patent walkthroughs. Research paper summaries. For readers comfortable with technical documentation and willing to engage with complexity.
A survey of Google's patent portfolio related to named entity recognition, covering the key patents from 2012 to present. Explains how the patent landscape maps to the evolution of entity-based search, with attention to the disambiguation and confidence scoring systems described across multiple filings.
Google Research has published papers describing how large-scale knowledge bases are constructed from web data. This article summarizes the key papers, explains the extraction and verification pipelines described, and discusses their implications for how web content contributes to entity knowledge.
Google's language models — BERT, MUM, and their successors — process text in ways that are fundamentally entity-aware. This article examines what the published architecture papers reveal about how these models handle entity disambiguation, co-reference resolution, and relationship extraction.
The Department of Justice antitrust proceedings produced sworn testimony and documentary evidence describing Google's internal systems. This article examines what those proceedings revealed about how entity data is used in search ranking, with specific attention to the Knowledge Graph's role.
The configuration guides provide technical depth on schema markup, Knowledge Graph connections, and entity signals for local businesses.
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