Augmented Search Queries Using Knowledge Graph Information

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.

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Google Knowledge Graph Reconciliation

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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