Boolean Searching: A Primer
[adapted from University at Albany Libraries]


Searching computer databases follows certain rules most often based on the principles of Boolean logic. Boolean logic refers to the logical relationship among search terms, and is named for the British mathematician George Boole.

Boolean logic consists of three logical operators:

Each operator can be visually described by using Venn diagrams, as shown below. 
OR
Venn diagram for OR
college OR university
Query:    I would like information about college. OR logic is most commonly used to search for synonymous terms or concepts.
Here is an example of how OR logic works:
Search terms Results
college 17,320,770
university 33,685,205
college OR university 33,702,660

OR logic collates the results to retrieve all the unique records containing one term, the other, or both.
The more terms or concepts we combine in a search with OR logic, the more records we will retrieve.

Venn diagram for OR

For example:
Search terms Results
college 17,320,770
university 33,685,205
college OR university 33,702,660
college OR university OR campus 33,703,082

AND
Venn diagram for AND
poverty AND crime

Query:    I'm interested in the relationship between poverty and crime.

Here is an example of how AND logic works:
Search terms Results
poverty 783,447
crime 2,962,165
poverty AND crime 1,677

The more terms or concepts we combine in a search with AND logic, the fewer records we will retrieve.

Venn diagram for AND

For example:
Search terms Results
poverty 783,447
crime 2,962,165
poverty AND crime 1,677
poverty AND crime AND gender 76

A few Internet search engines make use of the proximity operator NEAR. A proximity operator determines the closeness of terms within a source document. NEAR is a restrictive AND. The closeness of the search terms is determined by the particular search engine. For example, NEAR in AltaVista (Power Search) is 10 words. As another example, Google defaults to proximity searching by default.


NOT
Venn diagram for NOT
cats NOT dogs
Query:    I want to see information about cats, but I want to avoid seeing anything about dogs. Here is an example of how NOT logic works:
Search terms Results
cats 3,651,252
dogs 4,556,515
cats NOT dogs 81,497

NOT logic excludes records from your search results. Be careful when you use NOT: the term you do want may be present in an important way in documents that also contain the word you wish to avoid.