This document discusses how Google uses structured data and annotations to power its search results. It describes Andrew Hogue's work using annotation frameworks to link unstructured data to a fact repository known as a knowledge graph. The document outlines several patents related to augmenting queries, generating related questions, and identifying structured data and candidate answer passages to provide contextual search results.

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Changes
In Structured
Data at
Google

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Bill Slawski
- Director of SEO Research at Go Fish Digital (https://gofishdigital.com/)

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Photo by Craig
Whitehead on Unsplash
I feel like a detective when researching SEO

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Researching
Expertise,
Authority, and
Trustworthiness
(EAT)
• Patents
• White Papers
• LinkedIn Profiles
• Google Scholar Profiles
• Twitter Streams
• Employee Resumes

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Andrew
Hogue’s
Resume
Andrew Hogue’s Engineers were responsible for a number of
Patents involving annotating Structured Information Unstructured
Data; this was known at Google as the Annotation Framework.

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Annotation Framework Patents
1) Generating Structured Information
2) Support for Object Search
3) Data Object Visualization
4) Data Object Visualization Using Maps
5) Query Language
6) Automatic Object Reference Identification and Linking in a Browseable Fact Repository
7) Browseable Fact Repository
8) ID Persistence Through Normalization
9) Annotation Framework
10) Object Categorization for Information Extraction
11) Modular Architecture for Entity Normalization
12) Attribute Entropy as a Signal in Object Normalization
13) Designating Data Objects for Analysis
14) Data Object Visualization Using Graphs
15) Determining Document Subject by Using Title and Anchor Text of Related Documents
16) Anchor Text Summarization for Corroboration
17) Unsupervised Extraction of Facts

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Annotation Framework
Abstract
A fact repository contains facts having attributes and
values and further having associated annotations,
which are used, among other things, to vet facts in
the repository and which can be returned in
response to a query.

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Fact Repository (Early
Knowledge Graph)

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Freebase from Metaweb

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Google’s Knowledge Graph

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Wikipedia Info boxes

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Movie with Tom Hanks
as Walt Disney?

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SERP Results Based on Structured Data
Storing semi-structured data
US Patent: 9,754,048

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Augmentation Queries
For example, augmentation queries can be
identified by mining query logs and click logs.
Using the query logs, for example, the
augmentation query subsystem can identify
common user queries. The click logs can be used
to identify which user queries perform best, as
indicated by the number of clicks associated with
each query. The augmentation query subsystem
stores the augmentation queries mined from the
query logs and/or the click logs in the
augmentation query store.
Query augmentation
US Patent: 9,916,366

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Related Questions (People Also Ask)
These Questions use a question graph
from query logs to build the questions
they show.
Generating related questions for search
queries
US Patent 9,679,027

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Google Experimental Searches
https://research.google.com/tables
https://toolbox.google.com/datasetsearch

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Semantics of Tables
WebTables: Exploring the Power of Tables on the Web
Applying WebTables in Practice

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Google Dataset Search
Google Developer’s Dataset Support Pages

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Structured Data and Answer Passages
Candidate answer passages
US Patent: 10,180,964
… wherein a first subset of passage unit selection criteria
applies to structured content and a second subset of passage
unit selection criteria applies to unstructured content; and
generating, from passage units that satisfy the set of passage
unit selection criterion, a set of candidate answer passages,
each candidate answer passage being eligible to be provided
as an answer passage with search results that identify the
resources determined to be responsive to the query and being
separate and distinct from the search results.

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Schema is Updating to Version 3.5
Read About it at: https://webschemas.org/docs/releases.html#v3.5

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Computerized systems and methods for extracting and storing information
regarding entities
US Patent # 10,198,491
Entity Extraction & Knowledge Graph Construction
Taken from Unstructured Data:
Entities
Entity Types
Relationships Between Classes
Contexts Between Entities

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Question Mug
Entity Extraction from
Unstructured Data incorporates
Entities and Contexts into a
________ __________?

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THANKS TO THE SPONSORS

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Thank you !
Thank you!