- Think of a research topic
- Break them up into major concepts - typically 3 or more - eg A, B, C
- Identify synonyms for each concept (A1,A2, A3 ; B1, B2, B3 ; C1, C2, C3
- Combine them in the following manner
We like many libraries have created videos on it as well.
Databases also encourage this search pattern
I am not sure how old this technique is, but around 2000ish? databases also started to encourage this type of structured search.
But are such search patterns really necessary or useful?
Our search tools in the past
- Metadata (including subject terms) + abstract only - did not including full text
- Precise searching - what you enter is what you get search
- low levels of aggregation - A "large database" would maybe have 1 million items if you were lucky
I will also pause to note that relevancy ranking of results could be available but when you have so few results that you could reasonably look through say 100 or less, you would just scan all the results, so whether it was ranked by relevancy was moot really.
Today's search environment has changedFast forward to today.
Also the fact you are searching full-text rather than just metadata changes a lot. If an article was about TEENAGERS, there is pretty good odds you could find TEENAGER and probably, YOUTH, ADOLESCENCE etc in the full text of the book or article as well, so you probably did not need to add such synonyms to pick them up in the result set anyway.
Even if you did a basic
A AND B AND C - you would have a reasonable recall, thanks to autostemming, full text matching etc.
All this meant you get a lot of results now even with a basic search.
Effect of full-text searching + relative size of index + related words
Don't believe this change in search tools makes a difference? Let's try the ebscohost discovery service for a complicated boolean search because unlike Summon it makes it easy to isolate the effect of each factor.
Let's try this search for finding studies for a systematic review
depression treatment placebo (Antidepressant OR "Monoamine Oxidase Inhibitors" OR "Selective Serotonin Reuptake Inhibitors" OR "Tricyclic Drugs") ("general practice" OR "primary care") (randomized OR randomised OR random OR trial)
Option 1 : Apply related words + Searched full text of articles - 51k results
Option 2 : Searched full text of articles ONLY - 50K results
Option 3 : Apply related words ONLY - 606 results
Option 4 : Both off - 594 results
The effect of apply related keywords is slight in this search example possibly because of the search terms used, but we can see full text matches make a huge difference.
Option 4 would be what you get for "old school databases". In fact, you would get less than 594 results in most databases, because Ebsco Discovery service has a huge index far larger than any such databases.
To check, I did an equivalent search in one of the largest traditional abstracting and indexing database Scopus and I found 163 results (better than you would expect based on the relative sizes of Scopus vs EDS).
But 163 is still manageable if you wanted to scan all results, so relevancy ranking can be poor and it doesn't matter as much really.
Web scale discovery services might give poor results with such searches
First, I am not convinced that people who say nested boolean improves the results of their search have actually done systematic objective comparisons or whether it is based on impression that I did something more complicated so the results must be better. I could be wrong.
But we do know that many librarians and experienced users are saying the more they try to carry out complicated boolean searches the worse the results seem to be in discovery services such as Summon.
Matt Borg of Sheffield Hallam University wrote of his experience implementing Summon.
He found that his colleagues reported "their searches were producing odd and unexpected results."
"My colleagues and I had been using hyper stylised searches, throwing in all the boolean that we could muster. Once I began to move away from the expert approach and treated Summon as I thought our first year undergrads might use it, and spent more time refining my results, then the experience was much more meaningful." - Shoshin
Notice that Summon like Google Scholar actually fits all 3 characteristics of a modern search I mentioned above that are least suited for such searches
- Full text search
- High levels of aggregation (typical libraries implementing Summon at mid-size universities would have easily 300 million entries)
- autosteming was on by default - quotes give a boost to results with exact matches.