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.

Hummingbird & the entity revolution

3,605 views

Published on

  • Be the first to comment

Hummingbird & the entity revolution

  1. 1. Bill Slawski SMX East 2014 (#smx #21A) October 1, 2014 (9:00am-10:15am)
  2. 2. When Sergey Gave Larry a Tour “We both found each other obnoxious,” Brin counters when I tell him of Page's response. "But we say it a little bit jokingly. Obviously we spent a lot of time talking to each other, so there was something there. We had a kind of bantering thing going." Page and Brin may have clashed, but they were clearly drawn together - two swords sharpening one another. The Birth of Google by John Battelle #smx #21A @bill_slawski
  3. 3. #smx #21A @bill_slawski
  4. 4. Larry Invents PageRank Improved Text Searching in Hypertext Systems (pdf) #smx #21A @bill_slawski
  5. 5. #smx #21A @bill_slawski
  6. 6. Sergey Invents DIPRE Extracting Patterns and Relations from the World Wide Web (pdf) #smx #21A @bill_slawski
  7. 7. #smx #21A @bill_slawski
  8. 8. Patterns! #smx #21A @bill_slawski
  9. 9. Andrew Hogue’s Team Andrew Hogue’s Resume #smx #21A @bill_slawski
  10. 10. Patterns! #smx #21A @bill_slawski
  11. 11. Gathering & Annotating Knowledge Browseable fact repository #smx #21A @bill_slawski
  12. 12. Search becomes Knowledge #smx #21A @bill_slawski
  13. 13. Google’s Knowledge Graph The Knowledge Graph #smx #21A @bill_slawski
  14. 14. #smx #21A @bill_slawski
  15. 15. Alexandria torpedo factory CC BY-SA 3.0 #smx #21A @bill_slawski
  16. 16. Google Starts a Conversation FAQ: All About The New Google “Hummingbird” Algorithm #smx #21A @bill_slawski
  17. 17. Entities become “Search Entities” Search entity transition matrix and applications of the transition matrix Relationships between Search Entities #smx #21A @bill_slawski
  18. 18. Which [lincoln]? #smx #21A @bill_slawski
  19. 19. Tracking Knowledge Information Each record (herein referred to as a tuple: <document , query, data> ) comprises a query submitted by users, a document reference indicating the document selected by users in response to the query, and an aggregation of click data for all users or a subset of all users that selected the document reference in response to the query. Propagating query classifications Using Query User Data to Classify Queries #smx #21A @bill_slawski
  20. 20. Google is  Viewing Entities as Search Entities (“lincoln as a person” is a query)  likely using an RDF (Resource Description Framework) schema for tracking information to calculate probabilities (based on user behavior) of things like what classification is meant by a query.  Searching for Patterns in Queries and on Pages to answer questions  Looking for Schema markup and schema-related facts and attributes information to create and understand context. #smx #21A @bill_slawski
  21. 21. Patterns! #smx #21A @bill_slawski
  22. 22. Query Revision Based on Context and Substitute Rules Synonym identification based on co-occurring terms #smx #21A @bill_slawski
  23. 23. For example, the user may enter the search query "What is the best place to find and eat Chicago deep dish style pizza?" In determining whether the term "restaurant" is a synonym for the query term "place", a synonym engine may evaluate the query term in the context of adjacent terms, such as "best" or "to," as well as non-adjacent terms, such as "Chicago" and "pizza." Such an evaluation may result in the decision that, in the context of the non-adjacent term "pizza," the term "restaurant" is a synonym of the query term "place." #smx #21A @bill_slawski
  24. 24. Knowledge Base Searches Identifying entities using search results #smx #21A @bill_slawski
  25. 25. The Future of the Knowledge Graph? Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion #smx #21A @bill_slawski
  26. 26. Incompleteness of Knowledge Graph #smx #21A @bill_slawski
  27. 27. Introducing the Knowledge Vault? Constructing and Mining Web Scale Knowledge #smx #21A @bill_slawski
  28. 28. Recovering Semantics of Tables on the Web (pdf) #smx #21A @bill_slawski
  29. 29. Open Language Information Extraction Open Language Learning for Information Extraction (PDF) #smx #21A @bill_slawski
  30. 30. <div itemscope itemtype ="http://schema.org/Movie"> <h1 itemprop="name"&g;Avatar</h1> <div itemprop="director" itemscope itemtype="http://schema.org/Person"> Director: <span itemprop="name">James Cameron</span> (born <span itemprop="birthDate">August 16, 1954)</span> </div> <span itemprop="genre">Science fiction</span> <a href="../movies/avatar-theatrical-trailer. html" itemprop="trailer">Trailer</a> </div> Getting started with schema.org #smx #21A @bill_slawski
  31. 31. Quizz: Targeted Crowdsourcing with a Billion (Potential) Users (pdf) #smx #21A @bill_slawski
  32. 32. Search + Knowledge, Sharpening One Another #smx #21A @bill_slawski
  33. 33. Thank you - Bill Slawski  Director of Search Marketing at GoFishDigital  Author at SEO by the Sea  Tweet me at https://twitter.com/bill_slawski  About this session (#smx #21A)  Connect Author Entities: https://plus.google.com/u/1/+BillSlawski/posts  Email me: bill.slawski@gofishdigital.com #smx #21A @bill_slawski

×