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Normalized Google distance

The normalized Google distance (NGD) is a semantic similarity measure derived from the number of hits returned by the Google search engine for a given set of keywords. Keywords with the same or similar meanings in a natural language sense tend to be "close" in units of normalized Google distance, while words with dissimilar meanings tend to be farther apart.

Applications, Measurement & Art

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Research this topic

Explore the main themes, entities and connections around Normalized Google distance. Start with the topic map, then use the sections below for research and deeper semantic analysis.

Explore this topic

Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Introduction

Google distribution and Google code

Properties

Applications

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

Normalized Google distance

Nodes26
Edges25
Triples21
Avg. degree1.92
Density0.076923
Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

Normalized Google distance

Top relations

related to Google distribution and Google code · 11
Normalized Google distance → Based, Bible, Google, In, King James, NGD, Other, Oxford English Dictionary, The, Wikipedia, World Wide Web
related to Introduction · 10
Normalized Google distance → For, Google, Macbeth, Namely, NCD, NGD, Objects, Shakespeare, The, There

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

ngd google search hits terms web shakespeare macbeth given distance number occur 000 normalized displaystyle together page 25 wordnet pages

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Normalized Google distancerelated to Google distribution and Google codeThe0.60section
Normalized Google distancerelated to Google distribution and Google codeGoogle0.60section
Normalized Google distancerelated to Google distribution and Google codeBased0.60section
Normalized Google distancerelated to Google distribution and Google codeIn0.60section
Normalized Google distancerelated to Google distribution and Google codeNGD0.60section
Normalized Google distancerelated to Google distribution and Google codeWorld Wide Web0.60section
Normalized Google distancerelated to Google distribution and Google codeOther0.60section
Normalized Google distancerelated to Google distribution and Google codeWikipedia0.60section
Normalized Google distancerelated to Google distribution and Google codeKing James0.60section
Normalized Google distancerelated to Google distribution and Google codeBible0.60section
Normalized Google distancerelated to Google distribution and Google codeOxford English Dictionary0.60section
Normalized Google distancerelated to IntroductionThe0.60section

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

    Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

    Min side: 3
    For writers, content strategists, SEOs, marketers and creators — from quick topic research to advanced semantic analysis.