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. It told us Google may look at query logs and structured data (table data and schema data) that are related to a site to create augmentation queries, and evaluate information about searches for those queries by comparing them to original queries for pages from that site. If search results from the augmentation queries do well in evaluations compared to search results from 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 oftentimes 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 provides insights into how it is growing and improving and may influence what we see on the web. A newly granted Google patent from last month is about how Google improves the amount of data the Google Knowledge Graph contains.

The process in that patent differs from the patent I wrote about in How the Google Knowledge Graph Updates Itself by Answering Questions. Taken together, they tell us about how the knowledge graph is growing and improving. Part of the process involves entity extraction which I covered in Entity Extractions for Knowledge Graphs at Google.

This new patent tells us that information making its way into the knowledge graph is not limited to content from the Web, but also can “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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