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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Google Scoring Gibberish Content to Demote Pages in Rankings?

This week, Google was awarded a patent that describes how they might score content on how much Gibberish it might contain, which could then be used to demote pages in search results. That gibberish content refers to content that might be representative of spam content.

The patent defines gibberish content on web pages as pages that might contain a number of high value keywords, but might have been generated through:

  • Using low-cost untrained labor (from places like Mechanical Turk)
  • Scraping content and modifying and splicing it randomly
  • Translating from a different language

Gibberish content also tends to include text sequences that are unlikely to represent natural language text strings that often appear in conversational syntax, or that might not be in text strings that might not be structured in conversational syntax, typically occur in resources such as web documents.

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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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Driving Directions vs. Reviews as Ranking Signals for Google Maps

In my college days, I cooked at some local restaurants (free meals made it an attractive option for a starving college student). One of the restaurants was in the center of town, at one end of Main Street, and it was a popular place for local residents who returned over and over. It had a great reputation, and word-of-mouth propelled advertising for the place. Another dining venue I worked at was outside of the center of town, nearby an interstate highway. It didn’t have a great reputation, and very few regular customers, except for people who would stop during mealtime from the busy interstate. The “food” sign from the highway attracted most of the traffic to its dining room.

Funny thing is that most of the regulars that frequented the first restaurant rarely had to look up its location, because it was so well known. Most of the people who visited the second restaurant had never been there before, and relied upon the highway sign. There’s another restaurant in that location now, and I have no doubt that many people find it via maps or navigation on their mobile devices or in-car navigation. I mention this because I have some issues with a recently granted Google patent.

A screenshot from the patent that shows the different parts of a ranking system for local search that includes directions and reviews.

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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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Joining Go Fish Digital

Something was missing, and I didn’t exactly know what it was. Around a year or so ago, I joined a big agency, and that gave me a chance to look at a lot of sites, provide in-depth consultation audits for a number of clients, perform monthly strategy reviews for others, inform the sales team on issues that might be helpful to address in proposals, and help other SEOs within the company when they asked for it.

I enjoyed doing these things, but there was something missing. I enjoyed working with the crew that I worked with as well. It’s great to work with people who are excited about the Web and about learning and growing. I’m now going to be working with a new crew who are filled with excitement and energy and innovation.

Go Fish Digital

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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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Getting Information about Search, SEO, and the Semantic Web Directly from the Search Engines