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Normalized Google distance

Normalized Google distance is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Normalized Google distance rather than just read about it. In short: 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.

Key takeaways

  • Normalized Google distance belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Normalized Google distance to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Normalized Google distance from memory before moving on to harder problems.

Reference excerpt

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. Specifically, the NGD between two search terms x and y is

NGD ⁡ ( x , y ) = max { log ⁡ f ( x ) , log ⁡ f ( y ) } − log ⁡ f ( x , y ) log ⁡ N − min { log ⁡ f ( x ) , log ⁡ f ( y ) } {\displaystyle \operatorname {NGD} (x,y)={\frac {\max\{\log f(x),\log f(y)\}-\log f(x,y)}{\log N-\min\{\log f(x),\log f(y)\}}}}

where N is the total number of web pages searched by Google multiplied by the average number of singleton search terms occurring on pages; f(x) and f(y) are the number of hits for search terms x and y, respectively; and f(x, y) is the number of web pages on which both x and y occur. If the N G D ( x , y ) = 0 {\displaystyle NGD(x,y)=0} then x and y are viewed as alike as possible, but if N G D ( x , y ) ≥ 1 {\displaystyle NGD(x,y)\geq 1} then x and y are very different. If the two search terms x and y never occur together on the same web page, but do occur separately, the NGD between them is infinite. If both terms always occur together, their NGD is zero. Example: On 9 April 2013, googling for "Shakespeare" gave 130,000,000 hits; googling for "Macbeth" gave 26,000,000 hits; and googling for "Shakespeare Macbeth" gave 20,800,000 hits. The number of pages indexed by Google was estimated by the number of hits of the search term "the" which was 25,270,000,000 hits. Assuming there are about 1,000 search terms on the average page this gives N = 25 , 270 , 000 , 000 , 000 {\displaystyle N=25,270,000,000,000} . Hence

N G D ( S h a k e s p e a r e , M a c b e t h ) = ( 26.95 − 24.31 ) / ( 44.52 − 24.63 ) = 0.13 {\displaystyle NGD(Shakespeare,Macbeth)=(26.95-24.31)/(44.52-24.63)=0.13} . "Shakespeare" and "Macbeth" are very much alike according to the relative semantics supplied by Google.

Introduction The normalized Google distance is derived from the earlier normalized compression distance. Namely, objects can be given literally, like the literal four-letter genome of a mouse, or the literal text of Macbeth by Shakespeare. The similarity of these objects is given by the NCD. For simplicity we take it that all meaning of the object is represented by the literal object itself. Objects can also be given by name, like 'the four-letter genome of a mouse,' or 'the text of Macbeth by Shakespeare.' There are also objects that cannot be given literally, but only by name, and that acquire their meaning from their contexts in background common knowledge in humankind, like "home" or "red". The similarity between names for objects is given by the NGD.

Google distribution and Google code The probabilities of Google search terms, conceived as the frequencies of page counts returned by Google divided by the number of pages indexed by Google (multiplied by the average number of search terms in those pages), approximate the actual relative frequencies of those search terms as actually used in society. Based on this premise, the relations represented by the normalized Google distance approximately capture the assumed true semantic relations governing the search terms. In the NGD, the World Wide Web and Google are used. Other text corpora include Wikipedia, the King James version of the Bible or the Oxford English Dictionary together with appropriate search engines.

Properties The following properties are proved in:

The NGD is roughly in between 0 and ∞ {\displaystyle \infty } . It can be slightly negative. For example, "red red" gives about 20% more hits of Google on the World Wide Web than "red". (Mid 2013 there were 4.260.000.000 hits for "red" and 5.500.000.000 hits for "red red". Presently, "red red" now returns far fewer results than "red".) If the N G D ( x , y ) ≥ 1 {\displaystyle NGD(x,y)\geq 1} then we view x and y as very dissimilar. The NGD is not a metric. The NGD is zero for x and y that are not equal provided x and y do always occur together on the same web page. From the NGD formula we see that it is symmetric. The triangle property is not satisfied by the NGD. However, these results are theoretic. It is hard to come up with practical examples of the World Wide Web using Google that violate the triangle property.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Normalized Google distance

Start with the simplest possible case. Write down what Normalized Google distance claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Normalized Google distance before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Normalized Google distance ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Normalized Google distance

In research
Normalized Google distance appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Normalized Google distance in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Normalized Google distance is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computational linguistics, Statistical distance, so understanding it makes those chapters shorter.
In everyday life
Look for Normalized Google distance outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Normalized Google distance in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Normalized Google distance means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Normalized Google distance out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Normalized Google distance in simple terms?

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, w…

Why does Normalized Google distance matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Normalized Google distance?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Normalized Google distance.

Tags

  • Computational linguistics
  • Statistical distance

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