How Google May Handle Question Answering when Facts are Missing

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”

How Google’s Knowledge Graph Updates Itself by Answering Questions

unsplash-logoElijah Hail

The Future of Search is in Providing Knowledge to Searchers through a Knowledge Graph

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.

Continue reading “How Google’s Knowledge Graph Updates Itself by Answering Questions”

Search Engine Queries May be Used to Identify Entity Attributes

How Search Engine Queries to Identify Entity Attributes

What are query stream ontologies, and how might they change search?

Search Engine Queries to identify entity attributes

Search engines trained us to use keywords when we searched – to try to guess what words or phrases might be the best ones to use to try to find something we are interested in. That we might have a situational or informational need to find out more about. Keywords were an important and essential part of SEO – trying to get pages to rank highly in search results for certain keywords found in search engine queries that people would search for. SEOs still optimize pages for keywords, hoping to use a combination of information retrieval relevance scores and link-based PageRank scores, to get pages to rank highly in search results.

With Google moving towards a knowledge-based attempt to find “things” rather than “strings”, we are seeing patents that focus upon returning results that provide answers to questions in response to search engine queries. One of those from January describes how query stream ontologies might be created from search engine queries, that can be used to identify entity attributes which could be used to respond to fact-based questions using information about those entities.

There is a white paper from Google co-authored by the same people who are the inventors of this patent published around the time this patent was filed in 2014, and it is worth spending time reading through. The paper is titled, Biperpedia: An Ontology for Search Applications

Continue reading “Search Engine Queries May be Used to Identify Entity Attributes”

Google Related Questions or ‘People Also Ask’ Patent

Google Related People Also Ask Questions

Google Related Questions or People Also Ask Questions Patent

When you search at Google, the answers you receive sometimes now include additional questions, that often have the label above them, “People Also Ask.” I was curious if I might be able to find a patent about these questions, and I saw that these “people also ask” questions were sometimes referred to as “related questions.”

An article at Moz today on the topic was interesting: Infinite ‘People Also Ask’ Boxes: Research and SEO Opportunities. The answers about how those related questions are decided upon seem to have a simpler origin as described in Google’s patent, but it is interesting comparing the ideas from that post with the patent.

I searched through Google patent search for “related questions” and I came up with a patent named, “Generating related questions for search queries”. When I looked at the screenshots that accompanied the patent, they appeared to be very similar to the “People also ask” type questions Google shows us today in search results.

Continue reading “Google Related Questions or ‘People Also Ask’ Patent”

Entities in the Google Knowledge Graph Search API for Google

Exploring The Google Knowledge Graph Search API

Google-favicon-2015

The Google Knowledge Graph Search API on a query for Google shows the following Entities and results scores for them. I thought they were diverse enough to be interesting and worth sharing. A couple of the ones listed seem odd, such as the Indian Action movie. “Thuppakki” and the Town in Kansas,”Topeka.” (It seems like there is a song titled, “Google Google” in the film Thuppakki, and in 2010 Topeka renamed itself “Google” to try to attract Google Fiber to the area.) We are told by Google that “Results with higher result scores are considered better matches.”

These are the Google Knowledge Graph Search API results on a search for Google:

Google “resultScore”: 292.863342
Google Chrome “resultScore”: 51.392109
X “resultScore”: 51.392109
Googleplex “resultScore”: 44.052853
Google China “resultScore”: 30.75222
Google Lively “resultScore”: 30.75222
DoubleClick “resultScore”: 29.141159
GV “resultScore”: 28.957876
Thuppakki “resultScore”: 28.693569
Google Store “resultScore”: 26.077885
“Google Japan” “resultScore”: 24.272602
DeepMind Technologies “resultScore”: 24.115602
Topeka “resultScore”: 23.718664
Rich Miner “resultScore”: 21.961121
Google Capital “resultScore”: 21.048887
Google Hacks “resultScore”: 21.003328
“Google Korea” “resultScore”: 20.818398
Barney Google and Snuffy Smith “resultScore”: 20.384176
Verily Life Sciences “resultScore”: 19.65727
Patrick Pichette “resultScore”: 19.614473

Continue reading “Entities in the Google Knowledge Graph Search API for Google”

Google Patents Context Vectors to Improve Search

For example, a horse to a rancher is an animal. A horse to a carpenter is an implement of work. A horse to a gymnast is an implement on which to perform certain exercises.
For example, a horse to a rancher is an animal. A horse to a carpenter is an implement of work. A horse to a gymnast is an implement on which to perform certain exercises.

One of the limitations of information on the Web is that it is organized differently at each site on the Web. As a newly granted Google patent about Context Vectors notes, there is no official catalog of information available on the internet, and each site has its own organizational system. Search engines exist to index information, but they have issues, as described in this new patent that make finding information challenging.

Limitations on Conventional Keyword-Based Search Engines

Continue reading “Google Patents Context Vectors to Improve Search”