Using controlled vocabulary terms to optimize your search

Tony Russell-Rose
2 min readSep 9, 2020

--

In the previous video we learnt how to use search suggestions to help us choose effective keywords for our search. In this video, we’ll look at techniques to make those terms more precise.

A key task in developing effective search strategies is choosing the right terms for the various facets of your information need. For example, if you are researching the topic of promoting physical activity to prevent obesity in older people, you might start with a search like this:

You could use this strategy to search Pubmed and other databases. But Pubmed offers a more effective way to search using a controlled vocabulary known as Mesh, which consists of terms that have been specifically selected for the purposes of indexing and retrieval.

To transform a keyword into a controlled vocabulary term using 2Dsearch we simply select add field tag, and then type in ‘Mesh’. This tells Pubmed to treat this as a Mesh term rather than a keyword. Not all keywords can be found in Mesh, so you may need to refer to Mesh documentation to see which terms are available. But the effect is that the result count decreases because the search is now more precise.

We can apply the same approach to other databases. Lens, for example, uses a controlled vocabulary derived from Microsoft’s Academic Graph. So when we open a search in Lens, we can adopt the same approach of converting keywords into controlled vocabulary terms. Lens uses is a different tag to express this ‘ field_of_study ‘, but the effect is the same: the result count decreases because the search is now more precise.

Using controlled vocabularies is a simple but highly effective technique for making your searches more accurate. Moreover, using field tags offers other powerful ways to refine and optimise your search. We’ll cover some of these in our next video.

Originally published at https://www.2dsearch.com on September 9, 2020.

--

--

Tony Russell-Rose

Technology innovator with extensive experience in search and information retrieval. PhD in AI / NLP. Specialist skills in user experience, HCI and human factors