What are Augmented Search Queries?
Last year, I wrote a post called Quality Scores for Queries: Structured Data, Synthetic Queries and Augmentation Queries, which told us that Google may look at query logs and structured data (table data and schema data) related to a site to create augmentation queries, and evaluate information about searches for those comparing them to original queries for pages from that site, and if the results of the augmentation queries do well in evaluations compared to the original query results, searchers may see search results that are a combination of results from the original queries and the augmentation queries.
Around the time that patent was granted to Google another patent that talks about augmented search queries was also granted to Google, and is worth talking about at the same time with the patent I wrote about last year. It takes the concept of adding results from augmented search queries together with original search results, but it has a different way of coming up with augmented search queries, This newer patent that I am writing about starts off by telling us what the patent is about:
This disclosure relates generally to providing search results in response to a search query containing an entity reference. Search engines receive search queries containing a reference to a person, such as a person’s name. Results to these queries are often times not sufficiently organized, not comprehensive enough, or otherwise not presented in a useful way.
Continue reading “Augmented Search Queries Using Knowledge Graph Information”
Exploring how the Google Knowledge Graph works can provide some insights into how is growing and improving and may influence what we see on the web. A newly granted Google patent from the end of last month tells us about one way that Google is using to improve the amount of data that the Google Knowledge Graph contains.
The process involved in that patent doesn’t work quite the same way as the patent I wrote about in the post How the Google Knowledge Graph Updates Itself by Answering Questions but taken together, they tell us about how the knowledge graph is growing and improving. But part of the process involves the entity extraction that I wrote about in Google Shows Us How It Uses Entity Extractions for Knowledge Graphs.
This patent tells us that information that may make its way into Google’s knowledge graph isn’t limited to content on the Web, but can also may “originate from another document corpus, such as internal documents not available over the Internet or another private corpus, from a library, from books, from a corpus of scientific data, or from some other large corpus.”
What Google Knowledge Graph Reconciliation is?
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“How Long is Harry Potter?” is asked in a diagram from a Google Patent. The answer to this vague question is unlikely to do with a length related to the fictional character but may have something to do with one of the best selling books or movies featuring Harry Potter.
When questions are asked as queries at Google, sometimes they aren’t asked clearly, with enough preciseness to make an answer easy to provide. How does Google Answer vague questions?
Question answering seems to be a common topic in Google Patents recently. I wrote about one not long ago in the post, How Google May Handle Question Answering when Facts are Missing
So this post is also on question answering but involves issues involving the questions rather than the answers. And particularly vague questions.
Continue reading “How would Google Answer Vague Questions in Queries?”
Search Using Structured Data
Structured Data is information that is formatted into a repository that a search engine can read easily. Some examples include XML markup in XML sitemaps and schema vocabulary found in JSON-LD scripts. It is distinct from semi-structured, and unstructured data that have less formatting.
A search engine that answers questions based upon crawling and indexing facts found within structured data on a site works differently than a search engine which looks at the words used in a query, and tries to return documents using unstructured data which contains the same words as the ones in the query; hoping that such a matching of strings might contain an actual answer to the informational need that inspired the query in the first place. Search using Structured Data works a little differently, as seen in this flowchart from a 2017 Google patent:
Continue reading “Google Patent on Structured Data Focuses upon JSON-LD”
Visiting Seattle to Speak about Structured Data
I spoke at SMX Advanced this week on Schema markup and Structured Data, as part of an introduction to its use at Google.
I had the chance to visit Seattle, and tour some of it. I took some photos, but would like to go back sometimes and take a few more, and see more of the City.
One of the places that I did want to see was Pike Place market. It was a couple of blocks away from the Hotel I stayed at (the Marriott Waterfront.)
It is a combination fish and produce market, and is home to one of the earliest Starbucks.
I could see living near the market and shopping there regularly. It has a comfortable feel to it.
Continue reading “Schema, Structured Data, and Scattered Databases such as the World Wide Web”
I recently bought a lemon tree and wanted to learn how to care for it. I started asking about it at Google, which provided me with other questions and answers related to caring for a lemon tree. As I clicked upon some of those, others were revealed that gave me more information that was helpful.
Last March, I wrote a post about Google Related Questions, Google’s Related Questions Patent or “People Also Ask” Questions.
Google Related Questions Patent Updated to Include a Question Graph
Continue reading “Google Related Questions now use a Question Graph”