Category Archives: Search Queries

Evolving Google Search Algorithms

When I’m looking for something at a search engine, I will often start out with a particular query and then depending upon the kinds of results I see I often change the query terms I use. It appears that Google has been paying attention to this kind of search behavior from people who search like me. A patent granted to Google earlier this month watches queries performed by a searcher during a search session, and may give more weight to the words and phrases used earlier in a session like that, and might give less weight to terms that might be added on as a session continues.

This patent seems like part of an evolution of algorithms from Google that has brought us to their Hummingbird update.

An old print from the 1880s showing a cat evolving into a catcher.

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Google’s Hummingbird Algorithm Ten Years Ago

Added 2013-11-10 – Google was granted a continuation version of this same patent (Search queries improved based on query semantic information) on November 5th, 2013, where the claims section has been completely re-written in some interesting ways. It describes using a substitute term for one of the original terms in the query, and using an inverse document frequency count to see how many times that substitute term appears in the result set for the modified version of the query and for the original version of the query. The timing of this update of the patent is interesting. The link below points to the old version of the patent, so if you want you can compare the claims sections.

Back in September, Google announced that they had started using an algorithm that rewrites queries submitted by searchers which they had given the code name “Hummingbird.” At the time, I was writing a blog post about a patent from Google that seemed like it might be very related to the update because the focus was upon re-writing long and complex queries, while paying more attention to all the words within those queries. I called the post, The Google Hummingbird Patent because the patent seemed to be such a good match.

Hummingbird Image from the Department of Forestry

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The Google Hummingbird Patent?

Google introduced a new algorithm by the name of Hummingbird to the world today at the garage where Google started as a business, during a celebration of Google’s 15th Birthday. Google doesn’t appear to have replaced previous signals such as PageRank or many of the other signals that they use to rank pages. The announcement of the new algorithm told us that Google actually started using Hummingbird a number of weeks ago, and that it potentially impacts around 90% of all searches.

A screenshot from the patent showing different elements and databases in use to better understand queries.

It’s being presented as a query expansion or broadening approach which can better understand longer natural language queries, like the ones that people might speak instead of shorter keyword matching queries which someone might type into a search box.

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How Google May Reform Queries Based on Co-Occurrence in Query Sessions

When you search, especially for topics that you know little about, chances are that you might not include the most relevant terms in your query, or you might use words that may have ambiguous meanings.

One of the areas where search engines focus a lot of attention upon is in reformulating queries through query suggestions and query expansion to help searchers better meet their situational and informational needs quickly.

"Google query suggestions on a search for [find airedale terrier puppies]"

When you search, you might see a number of query suggestions at the bottom of the results that were first returned, like the ones above on a search for [find airedale terrier puppies]. Or a search engine might include synonyms or substitute queries to expand your original query.

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Relationships between Search Entities

When I talk about, or write about entities, it’s normally in the context of specific people, places, or things. Google was granted a patent recently which discusses a different type of entity, in a more narrow manner. These entities are referred to as “search entities”, and the patent uses them to predict probabilities and understand the relationship between them better. This kind of analysis might result in some pages ranking higher than they otherwise might because of their similarities to other sites, and in some sets of search results favoring fresher results as well.

google-transition-probabilities

These search entities can include:

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How Google May Substitute Query Terms with Co-Occurrence

But I’m a substitute for another guy
I look pretty tall but my heels are high
The simple things you see are all complicated
I look pretty young, but I’m just backdated, yeah

- Peter Townsend

When you search at Google, how easy is it to find what you’re looking for? Do you search again, but try different but related words if your first attempt doesn’t uncover pages that you find useful?

If I search for “car repair” and follow it up on a search for “auto repair,” I would suspect that I would see a lot of the same pages, but perhaps not in the same order. I would also expect to see local search results for both, and I do. The local search results aren’t in the exact same order either. Some words or phrases do make good substitutes for others though, as can be seen in the image below:

A comparison of co-occurring terms for 'french open' and 'frenchopen'.

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Context is King: Google Parameterless Searches

In the very near future, you may be able to perform searches at Google without bothering to type or speak a query. Instead, you might be able to just shake your phone, or hold down a button for a certain amount of time, and tell your phone something like “search now”. Known as parameterless searches, this type of search can depend upon the context within which the search is performed.

Screenshot from the patent showing someone shaking a phone, context information for that search, and a search result

For instance, imagine being driven to work at 50mph, and you shake your phone. It tells you that there’s congestion ahead, and offers an alternative route.Or it shows you a map with color-coded traffic information for different streets nearby according to traffic conditions. Or, you may have an appointment with a client made by email and included on your calendar, and you want to find and check the email to make sure that you have the right phone number. It could show the number and offer to make the call on your behalf. If you regularly take a train at around 8:00 am on weekday mornings, shaking your phone at 7:50 am might trigger a realtime schedule for the rails.

Context information for a parameterless search could include things such as:

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The Noise Becomes the Signal – Voice Queries and Accent Scores

Might a twang or a drawl influence the search results you see at Google? If you’re prone to calling an elevator a lift, and tend to speak the Queen’s English in an accent similar to hers, you might see different search results than if you grew up in the Bronx or in New Orleans. If you sport a Polish accent, or a Spanish one, and you perform voice searches on your phone, would receiving results in Polish or in Spanish because of your accent be a problem or a benefit? If your accent is Australian, and you search for “football” while in the US, would it surprise you to see some Australian Rules Football results returned to you?

Search engines have been using something called an Automated Search Recognition (“ASR”) engine to try to eliminate or reduce accents in voice searches by treating those as if they were noise. But the value of that noise might also be recognized as another signal that might improve search results.

A new patent was granted to Google yesterday that explores the topic in more depth. For instance, it provides this example of how a search engine might use such accent information:

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