Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
#SMX #15A @bill_slawski
Advanced Technical SEO: Schema & Structured Data, JavaScript
Schema,
Structured Data &
Scattered
D...
#SMX #15A @bill_slawski
Sergey Brin at the Web 2.0 Conference 2005.
Credit: James Duncan Davidson/O'Reilly Media, Inc.
Sou...
#SMX #15A @bill_slawski
Extracting Patterns and Relations from Scattered Databases
Such as the World Wide Web*
http://ilpu...
#SMX #15A @bill_slawski
The Vision Behind Brin’s DIPRE
If these chunks of information could be extracted from
the World Wi...
#SMX #15A @bill_slawski
Google Maps: A Proof of Concept Semantic Database
Generating structured information
http://patft.u...
#SMX #15A @bill_slawski
Structured Data Collected About Local Entities
•Name,
•Phone number,
•Address,
•Business hours,
•R...
#SMX #15A @bill_slawski
Share these #SMXInsights on your social channels!
#SMXInsights
 Example bullet text
– Example bul...
#SMX #15A @bill_slawski
Table Search at Google
https://research.google.com/tables?hl=en&ei=vKQBW8idBcLVpgOe3KXQDw&q=longes...
#SMX #15A @bill_slawski
The WebTables Project at Google
Because each relational table has its own “schema” of labeled and ...
#SMX #15A @bill_slawski
The Webtables Project
Google Experimental Table Search
WebTables: Exploring the Power of Tables on...
#SMX #15A @bill_slawski
Share these #SMXInsights on your social channels!
#SMXInsights
 Example bullet text
– Example bul...
#SMX #15A @bill_slawski
Using the Web as a Database
In 2005, Google publipublished a blog post
#SMX #15A @bill_slawski
Using the Web as a Database
In 2005, Google publipublished a blog post
“With the Knowledge
Graph,
...
#SMX #15A @bill_slawski
Using the Web as a Database
In 2005, Google publipublished a blog post
From https://en.wikipedia.o...
#SMX #15A @bill_slawski
Question-Answer Queries
Identifying entities using search results
#SMX #15A @bill_slawski
Schema Markup
I http://schema.org/TouristAttraction
#SMX #15A @bill_slawski
Schema Extensions
I https://www.gs1.org/1/smart-search-demo/
#SMX #15A @bill_slawski
More Schema Extensions
I http://www.edmcouncil.org/financialbusiness
#SMX #15A @bill_slawski
Crowdsourcing Ontologies with Biperpedia
We describe Biperpedia, an ontology with 1.6M (class, att...
#SMX #15A @bill_slawski
Schema Resources
1. Semantic Search Marketing – Aaron Bradley’s Google+ Community.
2. Schema.org E...
#SMX #15A @bill_slawski
Share these #SMXInsights on your social channels!
#SMXInsights
 Example bullet text
– Example bul...
LEARN MORE: UPCOMING @SMX EVENTS
THANK YOU!
SEE YOU AT THE NEXT #SMX
Bill Slawski
SEO by the Sea
Go Fish Digital
#SMX #15A...
Upcoming SlideShare
Loading in …5
×

Smx advanced-william-slawski-final

4,762 views

Published on

Schema, Structured Data, and Scattered Databases such as the World wide web

Published in: Business
  • Be the first to comment

Smx advanced-william-slawski-final

  1. 1. #SMX #15A @bill_slawski Advanced Technical SEO: Schema & Structured Data, JavaScript Schema, Structured Data & Scattered Databases Such as the World Wide Web
  2. 2. #SMX #15A @bill_slawski Sergey Brin at the Web 2.0 Conference 2005. Credit: James Duncan Davidson/O'Reilly Media, Inc. Source: https://www.flickr.com/photos/x180/50329318/in/set-1076331/Inventor of DIPRE (DIPRE - Dual Iterative Pattern Relation Expansion))
  3. 3. #SMX #15A @bill_slawski Extracting Patterns and Relations from Scattered Databases Such as the World Wide Web* http://ilpubs.stanford.edu:8090/421/1/1999-65.pdf *A provisional patent filed by Sergey Brin on March 10, 1999
  4. 4. #SMX #15A @bill_slawski The Vision Behind Brin’s DIPRE If these chunks of information could be extracted from the World Wide Web and integrated into a structured form, they would form an unprecedented source of information.
  5. 5. #SMX #15A @bill_slawski Google Maps: A Proof of Concept Semantic Database Generating structured information http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2= HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum. htm&r=1&f=G&l=50&s1=7,788,293.PN.&OS=PN/7,788,293&RS =PN/7,788,293
  6. 6. #SMX #15A @bill_slawski Structured Data Collected About Local Entities •Name, •Phone number, •Address, •Business hours, •Reservations policy, •Parking availability, •Acceptable payment options, •Other information
  7. 7. #SMX #15A @bill_slawski Share these #SMXInsights on your social channels! #SMXInsights  Example bullet text – Example bullet text #SMXInsights 1. Brin’s 1999 Dipre Algorithm Extracted Patterns & Relations from the Web 2. Google Maps Did the Same for Local Entities
  8. 8. #SMX #15A @bill_slawski Table Search at Google https://research.google.com/tables?hl=en&ei=vKQBW8idBcLVpgOe3KXQDw&q=longest+wooden+pier+in+California Query: What is the longest Wooden Pier in California?
  9. 9. #SMX #15A @bill_slawski The WebTables Project at Google Because each relational table has its own “schema” of labeled and typed columns, each such table can be considered a small structured database. The resulting corpus of databases is larger than any other corpus we are aware of, by at least five orders of magnitude.
  10. 10. #SMX #15A @bill_slawski The Webtables Project Google Experimental Table Search WebTables: Exploring the Power of Tables on the Web Applying WebTables in Practice - Research - Google Introducing Structured Snippets, now a part of Google Web Search
  11. 11. #SMX #15A @bill_slawski Share these #SMXInsights on your social channels! #SMXInsights  Example bullet text – Example bullet text #SMXInsights 1. The Webtables Project Learns Semantics From Data Tables Across the Web 2. Relational Tables are Considered Small Structured Databases 3. Tables that do well in Table Search may lead to Structured Snippets (Needs Testing!)
  12. 12. #SMX #15A @bill_slawski Using the Web as a Database In 2005, Google publipublished a blog post
  13. 13. #SMX #15A @bill_slawski Using the Web as a Database In 2005, Google publipublished a blog post “With the Knowledge Graph, we’re continuing to go beyond keyword matching to better understand the people, places and things you care about.”
  14. 14. #SMX #15A @bill_slawski Using the Web as a Database In 2005, Google publipublished a blog post From https://en.wikipedia.org/wiki/Poland
  15. 15. #SMX #15A @bill_slawski Question-Answer Queries Identifying entities using search results
  16. 16. #SMX #15A @bill_slawski Schema Markup I http://schema.org/TouristAttraction
  17. 17. #SMX #15A @bill_slawski Schema Extensions I https://www.gs1.org/1/smart-search-demo/
  18. 18. #SMX #15A @bill_slawski More Schema Extensions I http://www.edmcouncil.org/financialbusiness
  19. 19. #SMX #15A @bill_slawski Crowdsourcing Ontologies with Biperpedia We describe Biperpedia, an ontology with 1.6M (class, attribute) pairs and 67K distinct attribute names. Biperpedia extracts attributes from the query stream, and then uses the best extractions to seed attribute extraction from text. ~ Biperpedia: An Ontology for Search Applications
  20. 20. #SMX #15A @bill_slawski Schema Resources 1. Semantic Search Marketing – Aaron Bradley’s Google+ Community. 2. Schema.org Extensions – Learn About How Extensions work 3. Schema.org Community Group – A Place to discuss changes to Schema
  21. 21. #SMX #15A @bill_slawski Share these #SMXInsights on your social channels! #SMXInsights  Example bullet text – Example bullet text #SMXInsights 1. Schema Extensions are an opportunity for Growth in many industries 2. Ontologies built from Query Streams like Biperpedia are Crowdsourced & Optimized for Search
  22. 22. LEARN MORE: UPCOMING @SMX EVENTS THANK YOU! SEE YOU AT THE NEXT #SMX Bill Slawski SEO by the Sea Go Fish Digital #SMX #15A @bill_slawski

×