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Did the Algorithm Behind How News Articles Rank at Google Just 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 originally filed in 2003.
Continue reading “Evolution of Google’s News Ranking Algorithm”
More Diversity in Search Results
Earlier this year, we were told that Google was making an effort to make the search results we see more diverse, by showing us fewer results from the same domains in response to a query. Search Engine Land covered that news with the post: Google search update aims to show more diverse results from different domain names.
Shortly before that news about more diverse results in organic search came out, Google was granted a patent in May which told us about how they might enforce category diversity in showing different points of interest in local search results, This post is about that effort to make local search results more diverse.
More Diversity At Google in 2013, in Past Search Results
Continue reading “How Google Enforces Category Diversity for Some Local Search Results”
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 Entity Extractions for Knowledge Graphs at Google.
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?”
I wrote about a similar patent in the post, Google Extracts Facts from the Web to Provide Fact Answers
This one introduces itself with the following statement, indicating a problem that Google may have with answering questions from the facts it may collect from the Web to fill its knowledge graph:
Embodiments relate to relational models of knowledge, such as a graph-based data store, can be used to provide answers to search queries. Such models describe real-world entities (people, places, things) as facts in the form of graph nodes and edges between the nodes. While such graphs may represent a significant amount of facts, even the largest graphs may be missing tens of millions of facts or may have incorrect facts. For example, relationships, edges or other attributes between two or more nodes can often be missing.
That is the problem that this new patent is intended to solve. The patent was filed in November of 2017. The earlier patent I linked to above was granted in June 2017. It does not anticipate missing or incorrect facts like this newer patent warns us about. The newer patent tells us about how they might be able to answer some questions without access to some facts.
It’s also reminding me of another patent that I recently wrote about on the Go Fish Digital Website. That post is titled, Question Answering Explaining Estimates of Missing Facts. Both the patent that post was about and this new patent include Gal Chechik, Yaniv Leviathan, Yoav Tzur, Eyal Segalis, as inventors (the other patent has a couple of additional inventors as well.)
Continue reading “How Google May Handle Question Answering when Facts are Missing”