How would Google Answer Vague Questions in Queries?

“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.

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Google Patent on Structured Data Focuses upon JSON-LD

Google JSON-LD example

Search Using Structured Data

Structured Data is information that is formatted into a repository that a search engine can read easily. Some examples include XML markup in XML sitemaps and schema vocabulary found in JSON-LD scripts. It is distinct from semi-structured, and unstructured data that have less formatting.

A search engine that answers questions based upon crawling and indexing facts found within structured data on a site works differently than a search engine which looks at the words used in a query, and tries to return documents using unstructured data which contains the same words as the ones in the query; hoping that such a matching of strings might contain an actual answer to the informational need that inspired the query in the first place. Search using Structured Data works a little differently, as seen in this flowchart from a 2017 Google patent:

Flow Chart Showing Structured Data in a Search

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Schema, Structured Data, and Scattered Databases such as the World Wide Web

Visiting Seattle to Speak about Structured Data

I spoke at SMX Advanced this week on Schema markup and Structured Data, as part of an introduction to its use at Google.

I had the chance to visit Seattle, and tour some of it. I took some photos, but would like to go back sometimes and take a few more, and see more of the City.

One of the places that I did want to see was Pike Place market. It was a couple of blocks away from the Hotel I stayed at (the Marriott Waterfront.)

It is a combination fish and produce market, and is home to one of the earliest Starbucks.

I could see living near the market and shopping there regularly. It has a comfortable feel to it.

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Google Related Questions now use a Question Graph

I recently bought a lemon tree and wanted to learn how to care for it. I started asking about it at Google, which provided me with other questions and answers related to caring for a lemon tree. As I clicked upon some of those, others were revealed that gave me more information that was helpful.

Last March, I wrote a post about Google Related Questions, Google’s Related Questions Patent or “People Also Ask” Questions.

Google Related Questions Patent Updated to Include a Question Graph

As Barry Schwartz noted recently at Search Engine Land, Google is now also showing alternative query refinements as ‘People Also Search For’ listings, in the post, Google launches a new look for “people also search for” search refinements. That was enough to have me look to see if the original Google Related Questions patent was updated. It was. A continuation patent was granted in June of last year, with the same name, but updated claims

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Semantic Keywords Research and Topic Models

Seeing Meaning with semantic keywords

I went to the Pubcon 2017 Conference this week in Las Vegas Nevada and gave a presentation about Semantic keywords and topic models based upon white papers and patents from Google. My focus was on things such as Context Vectors and Phrase-Based Indexing.

I promised in social media that I would post the presentation on my blog so that I could answer questions if anyone had any.

I’ve been doing Semantic keywords and topic models research like this for years, where I’ve looked at other pages that rank well for keyword terms that I want to use, and identify phrases and terms that tend to appear upon those pages, and include them on pages that I am trying to optimize. It made a lot of sense to start looking at semantic topic models research after reading about phrase based indexing in 2005 and later.

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Google Extracts Facts from the Web to Provide Fact Answers

When Google crawls the Web, it extracts facts from content on the pages it finds as well as links on pages. How much information does it extract about facts on the Web? IN Providing fact answers? Microsoft showed off an object-based search about 10 years ago, in the paper, Object-Level Ranking: Bringing Order to Web Objects..

The team from Microsoft Research Asia tells us in that paper:

Existing Web search engines generally treat a whole Web page as the unit for retrieval and consuming. However, there are various kinds of objects embedded in the static Web pages or Web databases. Typical objects are products, people, papers, organizations, etc. We can imagine that if these objects can be extracted and integrated from the Web, powerful object-level search engines can be built to meet users’ information needs more precisely, especially for some specific domains.

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