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Slawski New Approaches for Structured Data:Evolution of Question Answering


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Google has moved from Search to Knowledge, and Focusing on Answering questions with knowledge graph entity information provides has led to answering queries with Knowledge graphs for those questions, with confidence scores between entities and other entities or attributes of entities, based upon freshness, reliabilillity, popularity, and proximity between an entity and another entity or an attribute.

Published in: Data & Analytics

Slawski New Approaches for Structured Data:Evolution of Question Answering

  1. 1. #pubcon Presented by: Bill Slawski (@bill_slawski) Director of SEO Research at Go Fish Digital New approaches for Structured Data: Evolution of Question-Answering
  2. 2. #pubcon@bill_slawski
  3. 3. #pubcon@bill_slawski Not This Web Photo By Robert Anasch At Unsplash
  4. 4. #pubcon@bill_slawski Not This Web, Anymore - A Link Graph
  5. 5. #pubcon@bill_slawski But Now This Web - An Entity Graph • Tuple = Object/Verb/Subject • Planet of the Apes Rated G • Planet of the Apes Released in 1968 • Planet of the Apes Directed by Franklin Schaffner • Planet of the Apes has actor Roddy McDowall • Planet of the Apes has actor Charlton Heston • Planet of the Apes has actor Kim Hunter
  6. 6. #pubcon@bill_slawski
  7. 7. #pubcon@bill_slawski Sergey Brin Filed a patent in 1999 on a way of crawling facts on the Web (Google’s 2nd Patent after PageRank?)
  8. 8. #pubcon@bill_slawski Extracting Patterns and Relations from the World Wide Web Did Sergey Want to Build Amazon?
  9. 9. #pubcon@bill_slawski Annotation Framework (Browseable Fact Repository=1st Knowledge Graph) Annotation Framework Patent Filed 8/2007
  10. 10. #pubcon@bill_slawski Annotations • Annotations are passed to requesting objects in response to queries and are used to determine search results. Annotations are also used to decide whether facts match and whether facts contain reasonable values.
  11. 11. #pubcon@bill_slawski MetaWeb’s FreeBase – June 2010
  12. 12. #pubcon@bill_slawski Knowledge Graph – May 2012
  13. 13. #pubcon@bill_slawski –June 2011 • You Can Subscribe to the Public Schema Mailing List at: ublic/public-schemaorg/ • To get involved in discussions and see information about monthly updates of It is a good way to keep on top of one of the fastest growing areas of SEO
  14. 14. #pubcon@bill_slawski Rich Results • The Google Developer pages detail information about how to get rich snippets in search results search/reference/overview • Rich Results are a carrot (reward) for including Schema Markup on your pages. Google provides specific implementation details for the Schema types listed on the next page. • They try to influence you to show richer search results which can help your content stand out in SERPs, and earn more clicks.
  15. 15. #pubcon@bill_slawski Types of Rich Results • Article How-To Review Snippet • Breadcrumb Job Posting Sitelinks Searchbox • Book Job Training Software App • Carousel Livestream Speakable • Corporate Contact Local Business Listing Course Subscription and Paywalled Content • Critic Review Logo Video • Dataset Movie • Employer Aggregate Rating Occupation • Event Product • Fact Check QnA • FAQ Recipe
  16. 16. #pubcon@bill_slawski Testing Tools • The Google Structured Data Testing Tool can be found at: data/testing-tool/u/0/ The Google Rich Results test can be found at: • Google Search Console reports on:
  17. 17. #pubcon@bill_slawski Table Data is Structured Data • Applying WebTables in Practice https://static.googleuserco hive/43806.pdf • Ten Years of WebTables • https://web.eecs.umich. edu/~michjc/papers/p2140 -cafarella.pdf
  18. 18. #pubcon@bill_slawski Structured Snippets • Introducing Structured Snippets, now a part of Google Web Search - structured-snippets-now.html
  19. 19. #pubcon@bill_slawski JSON-LD from Schema is Structured Data
  20. 20. #pubcon@bill_slawski Structured Data & Entities in Augmented Queries
  21. 21. #pubcon@bill_slawski The Evolution of Featured Snippets The First Step: Just the facts, fast Google Q&A - 2005
  22. 22. #pubcon Photo by Johannes Plenio on Unsplash
  23. 23. #pubcon@bill_slawski Facts From the Browseable Fact Repository • Extracting Facts from Documents (1) Extract facts, i.e., (subject, attribute, object) triples, from webpages to identify values of attributes, i.e., “objects” in the extracted triples. (2) Learn about patterns associated with those facts and attributes (3) Score Additional Facts from the Webpages
  24. 24. #pubcon@bill_slawski Answer Stores Natural Language Search Results for Intent Queries
  25. 25. #pubcon@bill_slawski Natural Language SERPs for Intent Queries • 1. Get Intent Questions from Authoritative Sources • 2. Get Intent Questions from Query Logs • 3. Convert Intent Questions into templates: a. What are the symptoms of XXXXXX b. What is the Treatment for XXXXXXXXXX • 4. Question and Answer Data Store is collected from authoritative sites Photo by Johann Siemens on Unsplash
  26. 26. #pubcon@bill_slawski Questions in Headings w/ Answers • Scoring candidate answer passages Granted 4/10/2018
  27. 27. #pubcon@bill_slawski Questions W/Topic Answers in Text Passages + Structured Data • Candidate answer passages Granted 1/15/2019
  28. 28. #pubcon@bill_slawski Interpreting Intent Behind Questions Evaluating semantic interpretations of a search query Granted July 16, 2019 Original ambiguous query: How long is Harry Potter? Semantic Interpretation: How long is the Book Harry Potter? Semantic Interpretation: How long is the movie Harry Potter? Semantic Interpretation: How tall is the character Harry Potter? Semantic Interpretation: How old is the character Harry Potter? Format = Template Question (entity) Image by Kristin Mitchell
  29. 29. #pubcon@bill_slawski The Evolving Knowledge Graph • Computerized systems and methods for extracting and storing information regarding entities Extract Entities, Related Entities and Entity Classes and Attributes of Entities Save Information in Tuples: Object/Verb/Subject Generate Association Scores between Entities and Attributes Based upon Factors
  30. 30. #pubcon@bill_slawski Association Scores • An association score may reflect a likelihood or degree of confidence that an attribute, attribute value, relationship, class hierarchy, designated context class, or other such association is valid, correct, and/or legitimate.
  31. 31. #pubcon@bill_slawski Association Scores in Knowledge Graph • A degree of Confidence that something is accurate or more likely to be true.
  32. 32. #pubcon@bill_slawski Weights Behind Association Scores • Association scores incorporate weights for each occurrence of an entity or context. • temporal weights (recent documents or occurrences) • reliability weights (more reliable sources, more heavily) • popularity weights (more popular sources, more heavily) • proximity weights (entities/contexts occurring in closer proximity to one another on a page, more heavily) Photo by Victor Freitas on Unsplash
  33. 33. #pubcon@bill_slawski How the Knowledge Graph Grows • Automatic discovery of new entities using graph reconciliation
  34. 34. #pubcon@bill_slawski Question Answering Using Knowledge GraphsNatural Language Processing With An N-Gram Machine Granted May 2, 2019 1. Use a question as a Query 2. Collect the SERPs from that Query 3. Break Pages from those SERPs into tuples 4. Build a Knowledge Graph From those tuples 5. Answer the Question Photo by Roman Kraft on Unsplash
  35. 35. #pubcon The Future? https://lod-
  36. 36. #pubcon@bill_slawski Thank You • Bill Slawski • Author at • Director of SEO Research at Go Fish Digital • Twitter: @bill_slawski Photo by Bill Slawski, Cardiff Tidal Pools