How Google’s Knowledge Graph Updates Itself by Answering Questions

knowledge graph updates

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How A Knowledge Graph Updates Itself

unsplash-logoElijah Hail

To those of us who are used to doing Search Engine Optimization (SEO), we’ve been looking at URLs filled with content, and links between that content, and how algorithms such as PageRank (based upon links pointed between pages) and information retrieval scores based upon the relevance of that content have been determining how well pages rank in search results in response to queries entered into search boxes by searchers. Web pages connected by links have been seen as information points connected by nodes. This was the first generation of SEO.

Chances are good that many of the methods that we have been using to do SEO will remain the same as new features appear in search, such as knowledge panels, rich results, featured snippets, structured snippets, search by photography, and expanded schema covering many more industries and features then it does at present.

Search has been going through a transformation. Back in 2012, Google introduced something it refers to as the knowledge graph, in which they told us that they would begin focusing upon indexing things instead of strings. By “strings,” they were referring to words that appear in queries, and in documents on the Web. By “things,” they were referring to named entities, or real and specific people, places, and things. When people searched at Google, the search engines would show Search Engine Results Pages (SERPs) filled with URLs to pages that contained the strings of letters that we were searching for. Google still does that, and is slowly changing to showing search results that are about people, places, and things.

Google started showing us in patents how they were introducing entity recognition to search, as I described in this post:
How Google May Perform Entity Recognition

They now show us knowledge panels in search results that tell us about the people, places, and things they recognize in the queries we perform. In addition to crawling webpages and indexing the words on those pages, Google is collecting facts about the people, places, and things it finds on those pages.

A Google Patent that was just granted in the past week tells us about how Google’s knowledge graph updates itself when it collects information about entities, their properties and attributes and relationships involving them. This is part of the evolution of SEO that is taking place today – learning how Search is changing from being based upon search to being based upon knowledge.

What does the patent tell us about knowledge? This is one of the sections that details what a knowledge graph is like that Google might collect information about when it indexes pages these days:

Knowledge graph portion includes information related to the entity [George Washington], represented by [George Washington] node. [George Washington] node is connected to [U.S. President] entity type node by [Is A] edge with the semantic content [Is A], such that the 3-tuple defined by nodes and the edge contains the information “George Washington is a U.S. President.” Similarly, “Thomas Jefferson Is A U.S. President” is represented by the tuple of [Thomas Jefferson] node 310, [Is A] edge, and [U.S. President] node. Knowledge graph portion includes entity type nodes [Person], and [U.S. President] node. The person type is defined in part by the connections from [Person] node. For example, the type [Person] is defined as having the property [Date Of Birth] by node and edge, and is defined as having the property [Gender] by node 334 and edge 336. These relationships define in part a schema associated with the entity type [Person].

Note that SEO is no longer just about how often certain words appear on pages of the Web, what words appear in links to those pages, in page titles, and headings, alt text for images, and how often certain words may be repeated or related words may be used. Google is looking at the facts that are mentioned about entities, such as entity types like a “person,” and properties, such as “Date of Birth,” or “Gender.”

Note that quote also mentions the word “Schema” as in “These relationships define in part a schema associated with the entity type [Person].” As part of the transformation of SEO from Strings to Things, The major Search Engines joined forces to offer us information on how to use Schema for structured data on the Web to provide a machine readable way of sharing information with search engines about the entities that we write about, their properties, and relationships.

I’m writing about this patent because I am participating in a Webinar online about Knowledge Graphs and how those are being used, and updated. The Webinar is tomorrow at:
#SEOisAEO: How Google Uses The Knowledge Graph in its AE algorithm. I haven’t been referring to SEO as Answer Engine Optimization, or AEO and it’s unlikely that I will start, but see it as an evolution of SEO

I’m writing about this Google Patent, because it starts out with the following line which it titles “Background:”

This disclosure generally relates to updating information in a database. Data has previously been updated by, for example, user input.

This line points to the fact that this approach no longer needs to be updated by users, but instead involves how Google knowledge graphs update themselves.

Updating Knowledge Graphs

I attended a Semantic Technology and Business conference a couple of year ago, where the head of Yahoo’s knowledge base presented, and he was asked a number of questions in a question and answer session after he spoke. Someone asked him what happens when information from a knowledge graph changes and it involves very sensitive information, and needs to be updated?

His Answer was that a knowledge graph would have to be updated manually to have new information placed within it.

That wasn’t a satisfactory answer because it would have been good to hear that the information from such a source could be easily updated, and it was a little difficult hearing that a search engine would need to be edited like a newspaper would be. This may have been the answer that the people from Yahoo believed was the proper answer, and I’ve been waiting for Google to answer a question like this to see what their answer would be. That made seeing a line like this one from this patent interesting:

In some implementations, a system identifies information that is missing from a collection of data. The system generates a question to provide to a question answering service based on the missing information, and uses the response from the question answering service to update the collection of data.

This would be a knowledge graph update, so that patent provides details using language that reflects that exactly:

In some implementations, a computer-implemented method is provided. The method includes identifying an entity reference in a knowledge graph, wherein the entity reference corresponds to an entity type. The method further includes identifying a missing data element associated with the entity reference. The method further includes generating a query based at least in part on the missing data element and the type of the entity reference. The method further includes providing the query to a query processing engine. The method further includes receiving information from the query processing engine in response to the query. The method further includes updating the knowledge graph based at least in part on the received information.

How does the search engine do this? The patent provides more information that fills in such details.

The approaches to achieve this would be to:

…Identifying a missing data element comprises comparing properties associated with the entity reference to a schema table associated with the entity type.

…Generating the query comprises generating a natural language query. This can involve selecting, from the knowledge graph, disambiguation query terms associated with the entity reference, wherein the terms comprise property values associated with the entity reference, or updating the knowledge graph by updating the data graph to include information in place of the missing data element.

…Identifying an element in a knowledge graph to be updated based at least in part on a query record. Operations further include generating a query based at least in part on the identified element. Operations further include providing the query to a query processing engine. Operations further include receiving information from the query processing engine in response to the query. Operations further include updating the knowledge graph based at least in part on the received information.

A knowledge graph updates itself in these ways:

(1) The knowledge Graph may be updated with one or more previously performed searches.
(2) The knowledge Graph may be updated with a natural language query, using disambiguation query terms associated with the entity reference, wherein the terms comprise property values associated with the entity reference.
(3) The knowledge Graph may use properties associated with the entity reference to include information updating missing data elements.

The patent that describes how Google’s knowledge graph updates themselves is:

Question answering to populate knowledge base
Inventors: Rahul Gupta, Shaohua Sun, John Blitzer, Dekang Lin, and Evgeniy Gabrilovich
Assignee: Google
US Patent: 10,108,700
Granted: October 23, 2018
Filed: March 15, 2013


Methods and systems are provided for a question answering. In some implementations, a data element to be updated is identified in a knowledge graph and a query is generated based at least in part on the data element. The query is provided to a query processing engine. Information is received from the query processing engine in response to the query. The knowledge graph is updated based at least in part on the received information.

Nicolas Torzec tweeted me a link to a paper published on the Google AI Blog, which shares a number of authors with this patent. It was posted in 2014 (a year after the patent this post is about was filed.) The paper explains in more detail how a knowledge graph might become more complete. As the Abstract of the paper tells us:

We discuss how to aggregate candidate answers across multiple queries, ultimately returning probabilistic predictions for possible values for each attribute. Finally, we evaluate our system and show that it is able to extract a large number of facts with high confidence.

The paper is Knowledge Base Completion via Search-Based Question Answering Reading this paper in addition to the patent is recommended. It presents a much more nuanced look at some of the issues that the people working upon this problem came across, and some of the solutions that they found to address those. One of the problems that they use to illustrate how this system works involves identifying the parents of Frank Zappa (His Band was named “The Mothers of Invention” which made that task have some issues unique, as well.)

It does seem like it is a difficult task trying to update a knowledge graph using questions and answers like this, and is a problem that faces some challenges. It is interesting seeing what stage we are at in having problems like this addressed – so read this paper carefully along with the patent.

We have been seeing other approaches that look at knowledge graphs from other directions such as:

3 Ways Query Stream Ontologies Change Search – this is about Google looking at query stream information to identify data that it can extract from the Web to use to build ontologies. By looking at searchers queries, in effect it is crowdsourcing information about topics that may be helpful in building those ontologies.

Constructing Knowledge Bases with Context Clouds – This tells us about how Google could look at unstructured content that it might be able to use to build up knowledge bases. We see statements like this from the patent the post is about:

Extending the number of attributes known to a search engine may enable the search engine to answer more precisely queries that lie outside a “long tail,” of statistical query arrangements, extract a broader range of facts from the Web, and/or retrieve information related to semantic information of tables present on the Web.

We haven’t reached the point where updating or building a knowledge base can be automated, and updating some knowledge graph information about some sensitive topics that change may be necessary still, but we have some examples of approaches that are underway towards such updates a possibility.

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28 thoughts on “How Google’s Knowledge Graph Updates Itself by Answering Questions”

  1. This is a great post about the fundamental shift in SEO and how search engines are getting smarter as they strive towards complete natural language processing. It’s imperative to manage business data and ensure clarity about your brand around the web.

  2. Hi Dan,

    That shift from strings to things, where it is not so much about matching keywords, but instead, about answering questions involving things. A search engine should be capable of filling in knowledge gaps in a knowledge graph about properties of an entity included in that knowledge graph, and by updating that graph, it becomes capable of answering questions about things that weren’t originally included in that knowledge graph. It is going to be interesting seeing how Google evolves beyond where it is at now.

  3. The connectedness of all things! 🙂

    I love that Google’s AI maybe asking questions on Q&A forums to fill in gaps! It does pose the new ethical question – are we the bots now?

  4. Hi Dixon,

    Maybe not a Q&A forum or an Ask Yahoo!, but lots of people ask Google questions daily. If Queries can be rewritten, and disambiguated, and new trending answers can be discovered, The knowledge Graph can be updated quickly when there are gaps in knowledge. We will see how that works. 🙂

  5. Just wait until Google Duplex (the 2 way conversation phone calling bot)is calling professors with data from Google + to get the answers to those tough questions. Maybe suggesting they write a blog post about the topic.

  6. Hi Loren,

    That would be the phone-a-friend option? That sounds similar to what Google did around 2 years ago to start providing better health-related featured snippets. They hired a number of doctors to write out answers. We shall see what Google does.

  7. very informative post. It’s a new thing to my knowledge. Thanks for sharing..

    Wikipedia is the most common warehouse of knowledge. I like to grab a sentence from your post.

  8. Hi Emmanuel,

    Thank you for asking. We don’t know how much Google might use DBpedia. You many find some papers from some academics, but Google doesn’t refer to DBPedia in any patents that I can recall. If you read the patent I wrote about, it does talk about the knowledge graph as if it is an entity on its own that may use other informational sources. Google does refer to Wikipedia in at least one patent where they talk about how they may extract information from information found on Wikipedia, and if you are curious about that, you can find it here:

    Extracting Facts for Entities from Sources such as Wikipedia Titles and Infoboxes

    Wikipedia and Google are separate entities, and while they do communicate, they fill separate purposes on the Web.

  9. Great job on the Google’s Knowledge Graph article Bill! It’s fascinating to learn about some of the complexities behind how the Google’s search engine gathers and distributes useful information. Very complex. I’m glad that we have great minds like yours on top of it!

  10. Awesome, you picked the great topic. I always love to read you post. Really appreciate you. Thanks for commenting session.

  11. This is a great insight into the Google Knowledge Graph. Thanks for this information. Search engines are becoming smarter day by day and the Knowledge Graph is one of the tools, contributing to it.

  12. Hi,
    Enjoyed reading the article above , really explains everything in detail,the article is very interesting and effective.Thank you and good luck for the upcoming articles.

  13. Thank You so much for this informative post Bill. Thanks for sharing how you are doing it and I am sure a lot of people will be helped through the resource you shared.

  14. Hi Bill,

    Insightful as always; thank you.

    I’m particularly intrigued by this paragraph;
    “Note that SEO is no longer just about how often certain words appear on pages of the Web, what words appear in links to those pages, in page titles, and headings, alt text for images, and how often certain words may be repeated or related words may be used. Google is looking at the facts that are mentioned about entities, such as entity types like a “person,” and properties, such as “Date of Birth,” or “Gender.””

    There are a number of takeaways from this but one which I’m sure will stir up debate is the part about words appearing in links. Even today we still see websites where SEO’s are building lots of anchor text links from PBN sites, convinced that this will reap rewards. These same people insist that SEO is all about the number of times they can introduce a keyword into the page content, either visible or in the code (I was only looking at a really poor example this morning).

    I was wondering, if this shift reduces the reliance on ‘on-page’ cues, does that mean that poorly optimised websites, with the relevant information scattered on the page, will rank, even if they don’t provide a good user experience? In other words, do you think people can we now schema to the top irrespective of UX?

    Thanks in advance.


  15. Hi Jonathan,

    The focus is now less upon how relevant a page may be for a specific query term, and how well a page may provide answers about specific entities, their properties, and relationships between entities. For instance, when someone asks Google, “In what year did einstein published his theory of relativity?” they are looking for an answer to when a specific person published a paper or book about physics. It is no longer about links or the use of Private Blog Networks (which is an attempt to manipulate ranking signals at Google.) At one point, the focus of rankings of pages was upon information retrieval relevance scores, and authority scores based upon things such as PageRank. Those things still have value today, but we don’t know how much longer they might. Google is shifting to a different way of weighting the value of pages (that won’t be manipulated by things such as PBNs) and the ability of those pages to anwer queries. What role might User Experience have? That will be seen. It is likely not a matter of click throughs and dwell time like some people hypothesize, though Google will likely find value in sending searchers to pages that might have high quality scores.

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