At Google’s 15th anniversary celebration last summer, shortly after Hummingbird was introduced, Tamar Yehoshua, Google VP of Search, showed us conversational search at Google by first demonstrating a query asking for “pictures of the Eiffel Tower”, and then following up with the query “How tall is It?”
In that second query, Google had to not only remember the Eiffel Tower was being asked about, but also to recognize the Eiffel Tower when it was being referred to as “it.” That is part of the new “conversational search” that Google is now engaging in, using something know by linguists as a “coreference.” I wanted to write about coreferences to clear up confusion that people might have had about them.
I was inspired to do that after reading an article from Eric Enge earlier today, where he wrote about Knowledge Graph Advances From Google
I’ve been exploring some of the different search results that we see at Google, including things such as rich snippets and question-answering results, and came across a couple of patent filings from Google that describe something called “Enriched Results.”
You’ve seen enriched results before. As the first of the patent filings tells us, these results tend to be for things such as:
- Airlines flights – live flight status information
- Athletes – player statistics
- Sports – League Scores
- Weather – local weather information
- Financial topics – financial data; and
- Television programs- programming schedules
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.
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.
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.
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.
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.
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.
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.
These search entities can include:
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: