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Evolution of Search

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A look at Google moving from indexing URLs to Indexing Data on the Web, and The Triggering of Direct Answers, Schema and Structured Snippets

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Evolution of Search

  1. 1. Evolution of Search Presented by: Bill Slawski Director of Search Marketing, Go Fish Digital @bill_slawski
  2. 2. Bio I am an Entity SEO by the Sea Writer Reads Patents for Fun 19 Years SEO
  3. 3. Evolution is possibly the wrong word.
  4. 4. Metamorphosis is Better
  5. 5. The Metamorphosis is one from indexing a Web of URLs to indexing a Web of Named Entities
  6. 6. Why Change? • The Inventor of the Web Updated it • Microsoft was going there • Google wanted to answer questions • Yahoo introduced a Web of Concepts • People search for Named Entities
  7. 7. The inventor of the Web…
  8. 8. Had more thoughts about the Web Design Issues of the Semantic Web (1998) http://www.w3.org/Desi gnIssues/Semantic.html
  9. 9. Involving the Web as a database… https://en.wikipedia.org/wiki/Semantic_Web
  10. 10. Microsoft was doing it
  11. 11. Object-level Vertical Search (2007) http://research.microsoft.com/en- us/people/jrwen/cidr2007.pdf
  12. 12. Google Q&A debuted 2005
  13. 13. Hello Direct Answers “Now we are increasingly able to provide direct answers -- even if you’re speaking your question using Voice Search -- which makes it quicker, easier and more natural to find what you’re looking for.” Google FORM 10-K for 2014 (Fiscal Year December 31, 2014)
  14. 14. Direct Answers “In each case we’re trying to get you direct answers to your queries because it’s quicker and less hassle than the ten blue links Google used to show.” We built Google for users, not websites Eric Schmidt – Google Europe Blog 9/6/2014
  15. 15. http://ilpubs.stanford.edu:8090/917/1/conceptMining-Techrep.pdf
  16. 16. Search engines like Google, Yahoo! and Bing have already started displaying richer information for some search queries, including maps and weather (for location searches), reviews and prices (for product search queries), and profiles (for people searches). A path already started?
  17. 17. What is a Named Entity? Specific People, Places, and Things including brands (Thanks, Rand!) And Entities show up in Queries!
  18. 18. According to an internal study of Microsoft, at least 20-30% of queries submitted to Bing search are simply name entities, and it is reported 71% of queries contain name entities. ~ Building Taxonomy of Web Search Intents for Name Entity Queries
  19. 19. Google Introduced a Knowledge Graph filled with Named Entities (May 16, 2012)
  20. 20. Some Entities are Historic Figures
  21. 21. Some are more modern Brands!
  22. 22. Good Bye 10 Blue Links
  23. 23. Metamorphosis of Search Results • Best Answers to Fact Queries • Answer Box Snippets • Intent Queries • Triggered Answer Boxes • Rich Content Answer Boxes • Entity Triggered Answer Boxes • Rich Snippets • Structured Snippets
  24. 24. Best Answers to Fact Queries Selecting the best answer to a fact query from among a set of potential answers Invented by: Douglas L. T. Rohde, Thomas W. Ritchford Assignee: Google Inc. US Patent 7,953,720 Filed: March 31, 2005 Granted: May 31, 2011
  25. 25. Fact Queries • A Fact Repository is searched to find an answer • Potential Answers are scored based upon confidence and importance. • The Best scored answer is displayed.
  26. 26. Universal Search = Invisible Tabs* *Danny Sullivan http://searchenginewatch.com/ sew/news/2064036/searching- with-invisible-tabs
  27. 27. Answer Box Snippets Determination of a desired repository Invented by Michael Angelo, David Braginsky, Jeremy Ginsberg, and Simon Tong US Patent Application 20070005568 Published January 4, 2007
  28. 28. Answer Box Snippets • Google may display an answer box from Different Repositories: Local, News, Books, Images, Shopping • They fulfill Universal Search Results • At 2006 SES, were referred to as “Vertical Creep into Search Results” and “Answer Box” Results.
  29. 29. Intent Queries Natural Language Search Results for Intent Queries International Filing Date: 23.05.2014 Inventors: Tomer Shmiel, Dvir Keysar, and Yonatan Erez
  30. 30. Natural Language Intent Queries • Natural language Questions • Answers from Authoritative Resources • Answers from a Data Store of headers and answers (Like in an FAQ) • Answers follow an Intent Format; “what are the symptoms of X”, “X Treatment” • Answers fit into a results format like Search Result Snippets
  31. 31. Look at the sources… http://www.webmd.com/a-to- z-guides/dehydration-adults
  32. 32. Triggering Answer Boxes Triggering Answer Boxes Invented by Tal Cohen, Ziv Bar-Yossef, Igor Tsvetkov, Adi Mano, Oren Naim, Nitsan Oz, Nir Andelman, Pravir K. Gupta US Patent Application 20150169750 Published June 18, 2015
  33. 33. Triggering Answer Boxes • Review Search Results for Any that might Trigger Answer Box Results; Search Result for “Duke Blue Devils” could be Football or Basketball, so answer box would be, too. • Review Topics Associated with Search Results (news, sports, weather, television) • These Answer box results might be personalized.
  34. 34. Rich Content for Answers Rich Content for Query Answers Pub. No.: WO/2015/102869 Invented by: Gal Chechik, Eyal Segalis, Yaniv Leviathan, and Yoav Izur International Filing Date: 15.12.2014
  35. 35. Rich Content for Answers • Answers may go beyond Text to Images, Video, Audio • Rich Results may be based upon contextual queries that combine the questions and answers • An image result for “What is the capital of California?” might use “capital of California, Sacramento” as the query
  36. 36. Entity Triggering Using an entity database to answer entity-triggering questions Inventors: Melissa K. Carroll, and John J. Lee US Patent 9,081,814 – Granted July 14, 2015
  37. 37. Entity Triggering • Similar to Fact Repository Approach • Google looks for entities in queries. • Google tries to including those entities in the answers. • “Identifying” the entities may help lead to answers • Answers may be values of attributes of those entities, such as, “What is the population of LA?”
  38. 38. Combining Data & URLs • Rich Snippets – Uses Data from Schema Vocabulary • Structured Snippets – Uses Data from Tables
  39. 39. Data + URLs (Rich Snippets) http://schema.org/
  40. 40. Rich Snippets Generating specialized search results in response to patterned queries Invented by Nicholas Brock Weininger, and Ramanathan V. Guha US Patent 7,593,939 Granted September 22, 2009
  41. 41. Rich Snippets
  42. 42. URLs + Tables (Structured Snippets)
  43. 43. WebTables: Exploring the Power of Tables on the Web - 2008 (pdf)
  44. 44. Structured Snippets Applying WebTables in Practice, by Sreeram Balakrishnan, Alon Halevy, Boulos Harb, Hongrae Lee, Jayant Madhavan, Afshin Rostamizadeh, Warren Shen, Kenneth Wilder, Fei Wu, Cong Yu, 7th Biennial Conference on Innovative Data Systems Research (CIDR ’15) January 4-7, 2015, Asilomar, California, USA
  45. 45. Structured Snippets • Use Tables on your pages filled with data • Use markup like Table Headings<th> for rows of the tables • Use Titles and captions descriptive of the Table’s data • Use Associated text on the Page the table is about to help Google better understand it.
  46. 46. Further Reading on Google Answers • Deep Learning - Teaching Machines to Read and Comprehend (Deepmind) • Information Extraction - Open Information Extraction from the Web (Wavii) • Tables - Applying WebTables in Practice (Alon Halevy) • Entities - Use These Tools To See What Entities Are On A Web Page (Barbara Starr)
  47. 47. Questions • Bill Slawski, Director of Search Marketing, Go Fish Digital • Editor of SEO by the Sea • https://plus.google.com/u/0/+BillSlawski/posts • https://twitter.com/bill_slawski • https://www.facebook.com/bill.slawski

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