Some patents and some white papers focus upon search queries, and how a search engine might respond to what a searcher might be looking for, and help to fulfill the informational and situational needs they might have.
It is often tempting to think of search queries in terms of the intent behind them, whether they are informational, transactional or navigational. I recommend that people read the paper A Taxonomy of Web Search by Andrei Broder (who is now at Google) to learn more about the intents behind Search Queries.
With the Web becoming more Semantic and being more about finding entities, it’s possible that looking at search queries to see if they trigger the appearance of an answer box or a featured snippet will become more common.
Some of the people who write patents for Google tend to stand out to me. One of those is Trystan Upstill.
I noticed that he has published another patent that looks really interesting, and worth reading. When I started following his patents, I read his doctoral thesis, Document ranking using web evidence which was really interesting, from the early days in his professional career.
It is from before he was listed as the inventor of a number of patents, that I also found interesting. I’ve written about a number of patents he has participated in creating as well because they often focus upon Site Quality, and I learn something from reading them and trying to understand them.
Here are posts from his patents which I have written about previously:
A new patent application from Google tells us about how the search engine may use context to find query suggestions before a searcher has completed typing in a full query. Think of Google as a Decision Engine, focused upon bringing searchers more information about interests they may have. After seeing this patent, I’ve been thinking about previous patents I’ve seen from Google that have similarities.
In general, the subject matter of this specification relates to identifying or generating augmentation queries, storing the augmentation queries, and identifying stored augmentation queries for use in augmenting user searches. An augmentation query can be a query that performs well in locating desirable documents identified in the search results. The performance of an augmentation query can be determined by user interactions. For example, if many users that enter the same query often select one or more of the search results relevant to the query, that query may be designated an augmentation query.
In addition to actual queries submitted by users, augmentation queries can also include synthetic queries that are machine generated. For example, an augmentation query can be identified by mining a corpus of documents and identifying search terms for which popular documents are relevant. These popular documents can, for example, include documents that are often selected when presented as search results. Yet another way of identifying an augmentation query is mining structured data, e.g., business telephone listings, and identifying queries that include terms of the structured data, e.g., business names.
These augmentation queries can be stored in an augmentation query data store. When a user submits a search query to a search engine, the terms of the submitted query can be evaluated and matched to terms of the stored augmentation queries to select one or more similar augmentation queries. The selected augmentation queries, in turn, can be used by the search engine to augment the search operation, thereby obtaining better search results. For example, search results obtained by a similar augmentation query can be presented to the user along with the search results obtained by the user query.
How Search Engine Queries to Identify Entity Attributes
What are query stream ontologies, and how might they change search?
Search engines trained us to use keywords when we searched – to try to guess what words or phrases might be the best ones to use to try to find something we are interested in. That we might have a situational or informational need to find out more about. Keywords were an important and essential part of SEO – trying to get pages to rank highly in search results for certain keywords found in search engine queries that people would search for. SEOs still optimize pages for keywords, hoping to use a combination of information retrieval relevance scores and link-based PageRank scores, to get pages to rank highly in search results.
With Google moving towards a knowledge-based attempt to find “things” rather than “strings”, we are seeing patents that focus upon returning results that provide answers to questions in response to search engine queries. One of those from January describes how query stream ontologies might be created from search engine queries, that can be used to identify entity attributes which could be used to respond to fact-based questions using information about those entities.
There is a white paper from Google co-authored by the same people who are the inventors of this patent published around the time this patent was filed in 2014, and it is worth spending time reading through. The paper is titled, Biperpedia: An Ontology for Search Applications
In October of 2015, a new algorithm was announced by members of the Google Brain team, described in this post from Search Engine Land – Meet RankBrain: The Artificial Intelligence That’s Now Processing Google Search Results One of the Google Brain team members who gave Bloomberg News a long interview on Rankbrain, Gregory S. Corrado was a co-inventor on a word vectors patent that was granted this August along with other members of the Google Brain team.