As mentioned in the previous section, databases do not understand natural language. They can only match strings of characters that you type in the search field, to strings of characters that indexed in their contents (e.g., title, author, phrases found in the full text of articles or abstracts)
Since library databases depend on precise search phrases to yield precise results, it helps to think of your topic in terms of the key concepts that define it.
In order to narrow down your topic, you add concepts to it to make it more specific. Here, we break those concepts apart so that we can recombine them in a way the databases can understand.
The divisions will not always be clean. Is "preventable fatalities" one concept or two? How about "Egyptian pyramids"? Once you start trying to think of synonyms and related terms, this will become clearer to you.
Again, databases can only match strings of characters that you input with strings of characters in their contents. So if you enter fatalities, articles that use the word deaths instead may not appear in your search results. You could design a Boolean search to include fatalities and deaths to retrieve results using both concepts.
Brainstorming synonyms and related terms can help.
To create a Concept Chart, use one column for each of the concepts you identified in your topic. Beneath each concept, add all the synonyms and related terms you brainstormed, as below.
Concept Chart for "effects of anti-bullying programs on the self-esteem of adolescent girls"
|Stand Up - Speak Out
|Bullying: Ignorance Is No Defense
Notice there are different ways to think of synonyms and related terms:
You may not be sure of the best combination of search terms until you have tried them all. Ambiguity and uncertainty are normal (and can even be good). Research, particularly searching, usually involves trial and error.