JD_Toims - 9:04 pm on Oct 12, 2013 (gmt 0)
Since it appears there's a definite shift in SEO and I haven't seen any threads or posts on these I thought it would be a good time to share them.
Bill Slawski at seobythesea.com has some great summaries for those who aren't patent readers, so I quoted/linked those as well as the patents below.
Synonym Identification Based on Co-occurring Terms
[What appears to be the Humming Bird Patent]
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.
Evaluation of Substitute Terms
The process used to find substitute terms focuses upon the use of the co-occurrence of words found on pages returned in response to a query, and to a potential substitute query. These candidate substitute terms might originally show up in documents ranking for the first query term, or in meta data associated with those documents.
For example, to find a potential substitute query terms for “cats,” terms that appear in documents ranking for “cats” may be explored. One of those might be “feline.” If we perform a search for “cats”, and look through the top 10 (or top 20, or even top 100) results for words that tend to co-occur on those pages, we might see words such as “furry”, “domesticated”, “carnivorous” and ” mammal” appear on a lot of the top pages returned for that query. If those are terms that tend to co-occur often in the results on a search for “cats,” they are considered co-occurring terms.
Generalized Edit Distance for Queries
This patent looks for co-occurring words within search sessions instead of on web pages or within search results for particular queries.
Query terms that might be similar are selected in part on how closely they might be related semantically. For example, It’s much more likely to see “become a dentist” followed by a query for “become a dental assistant,” instead of being followed by “become a doctor.” in a set of query sessions. It’s likely that we’ll see people change their queries in such a manner when they are performing searches in a search session.
Search Entity Transition Matrix and Applications of the Transition Matrix
These search entities can include:
When I sit and think about all the preceding concepts and how they could work together it seems to explain quite a bit of what appear to be inconsistencies and oddities in the results people are reporting lately.