Google Feud and Autocompletion

When you search using Google, you can't help but notice that Google autocompletes your queries. For example, if you type DRAGON FRUIT and then click in the search box, you'll see suggested queries like these:

• dragon fruit how to eat
• dragon fruit tree
• dragon fruit side effects 
• dragon fruit origin
• dragon fruit recipes
• dragon fruit calories
• dragon fruit taste
• red dragon fruit

Sometimes these guide your search, cluing you into things about dragon fruit you weren't aware of or the right words to use. Supposedly, the results are due to the popularity of terms used when searching for dragon fruit. For more information read this article.

This feature has been around a long time and Google wasn't the first to use it.

There's even a game that uses autocompleted queries: Google Feud

The game is instructive and aligns nicely with an Information Fluency observation: the "One in Five Rule." 

When creating queries your goal is to find a combination of keywords that are an exact match for the wording in the documents you seek. Different authors use different words to describe the same thing. There isn't strong evidence for the 1:5 ratio, but there are, on average, 13 synonyms per word, (see InfoWorld Dec 15, 1986). Considering that Web pages employ a limited range of language, it seems reasonable one's chances of searching for exactly the same keyword as a Web page author may be closer to 1 in 5. If an author uses a highly technical term, the ratio increases and you may never match it. In that case you have to search using contextual clues. The 1 in 5 "rule of thumb" means you should expect to revise your query more than once before matching keywords with an author. [Source]

Play Google Feud. How often do you match with any of the suggested queries?  There are four categories: CULTURE, PEOPLE, NAMES, QUESTIONS. I did poorly, matching the suggested keywords 7 times in 31 tries across the different categories. Interestingly, that's not much better than 1:5.

This experience opens a searcher's eyes to the possibilities that there are other words to use in queries than one person can think of. Finding the most relevant results depends on keywords another person has used. How you find those is a combination of trial and error, trying to think like the author whose words you are looking for and searching snippet results for clues to related words.

Revisiting Optimal Number of Keywords

When building a query, how many keywords is enough and what number is too many?

There really are no absolutes here because so many variables are involved, but there are some guiding principles which I've found consistently helpful.

The first item on the Query Checklist remains highly relevant: 'How many key concepts are contained in the question?' If you are merely interested in mp3 players, there's one concept contained in two words. On the other hand, if you want to know 'How many buffalo are there today in North America?', then you have four key concepts with which to contend:

what - many (number), buffalo

where - North America

when - today

Generally, the more defined the objective, the more concepts there are. Searching for just one or more than three concepts both may be problematic because of literal matching.

Literal matching: Too few terms

One-word queries are often ineffective because they match so much information that is irrelevant. The reason results are irrelevant is that the search wasn't defined sufficiently to begin with. I may want to find information on buffalo, but if I search only using that word, I will have to browse through a lot of information I may not care about. Interestingly, about half the college-aged subjects taking College Board's ICT test a couple years ago used one-word queries (citation needed--anyone up for the challenge?). One-word or single concept queries are probably good enough if you want to do a broad scan of the information landscape pertaining to a product, a person, an idea, etc., but they tend to cast a very wide net and consequently slow you down.

Part of the problem with a word like buffalo is that it has more than one meaning. You only had one meaning in mind but the search engine doesn' t know that because it looks for literal matches. This is where I usually introduce the 1 in 5 rule (although it's more of a phenomenon of language than it is a rule). On average, there are five terms that may be used for the concept you have in mind. You say buffalo, others say bison. There are probably only a couple more (ungulate anyone?), but in some cases there could be many more alternate terms (this happens especially with verbs).

Literal matching: Too many terms

Trying to match all the same words used by an author becomes increasingly difficult the more words you use. The beauty of search engines is that when you use words in a meaningful context, they tend to retrieve the meaning you have in mind. That's why a search for many buffalo North America doesn't yield information about buffalo wings or the Buffalo Bills football team. But those may not be the exact words an expert used when writing on the subject I'm researching. He or she may have used population instead of many, bison instead of buffalo. Proper nouns such as North America are more likely to be matched. The more terms you use, the less likely it becomes you will find an exact match.

I've had a lot of success searching for two or three concepts in my career. It requires keeping important concepts in mind that aren't really needed in the query--such as today in the buffalo example. Scanning the results, I look for current data, not information from the 1800's.

Sure, you can use queries containing more than three concepts, but unless you have a good idea what words an expert used, you're pushing your luck. Probability is against you. You're better off keeping your query simple.