User-Specific Knowledge Graphs to Support Queries and Predictions

A recently granted patent from Google covers supporting querying and predictions. It does this by focusing on user-specific knowledge graphs.

Those User Specific Knowledge Graphs can be specific to particular users.

Google can use those graphs to provide results in response to one or more queries submitted by the user, and/or to show data that is relevant to the user.

I thought of another patent I wrote about when I saw this patent, in the post Answering Questions Using Knowledge Graphs. In that one, Google may search on a question someone asks by building a knowledge graph from search results, to use to find the answer to their question.

So Google doesn’t just have one knowledge graph but may use many knowledge graphs.

New ones for questions that may be asked, or for different people asking those questions.

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Evolution of Google’s News Ranking Algorithm

Image: Photo by Nathan Dumlao on Unsplash

Did the Algorithm Behind How News Articles Rank at Google Change?

A Google Patent about how news articles are ranked by Google was updated this week, and in this case it suggests how entities in those documents can have an impact on ranking.

How Have News Articles Been Ranked at Google?

This patent was first filed in 2003.

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How Google Enforces Category Diversity for Some Local Search Results

More Diversity in Search Results

Earlier this year, Google told us that it was trying to make search results more diverse with fewer results from the same domains in response to a query. Search Engine Land wrote about it with the post: Google search update aims to show more diverse results from different domain names.

Not long before telling us that, Google was granted a patent in May about how they could enforce category diversity in showing different points of interest in local search results, This post is about Google making local search results more diverse.

More Diversity At Google in 2013, in Past Search Results

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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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