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Ranking in Google Since The Advent of The Knowledge Graph

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Ranking in Google Since The Advent of The Knowledge Graph

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A Two Person Panel Discussion/Presentation by Bill Slawski and Barbara Starr On June 23, 2015

The Lotico Semantic Web of San Diego
The SEO San Diego Meetup
The SEM San Diego Meetup
http://www.meetup.com/InternetMarketingSanDiego/events/222788495/

User experience drives search engines, and hence their results. Search Engine Result Presentation/Placements naturally follow that route.

This means that search results are no longer exclusively based on just ranking criteria. Amongst other critical factors is understanding the notion of 'ordering vs ranking', the impact of context and many others.

A Two Person Panel Discussion/Presentation by Bill Slawski and Barbara Starr On June 23, 2015

The Lotico Semantic Web of San Diego
The SEO San Diego Meetup
The SEM San Diego Meetup
http://www.meetup.com/InternetMarketingSanDiego/events/222788495/

User experience drives search engines, and hence their results. Search Engine Result Presentation/Placements naturally follow that route.

This means that search results are no longer exclusively based on just ranking criteria. Amongst other critical factors is understanding the notion of 'ordering vs ranking', the impact of context and many others.

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Ranking in Google Since The Advent of The Knowledge Graph

  1. 1. June 23, 2015 Semantic Web Meetup, SEO San Diego Meetup, SEM San Diego Meetup Courtyard San Diego Old Town #SEOSDM
  2. 2. A Two Person Panel Discussion/Presentation by Bill Slawski and Barbara Starr User experience drives search engines, and hence their results. Search Engine Result Presentation/Placements (SERPs) naturally follow that route. This means that search results are no longer exclusively based on just ranking criteria. Amongst other critical factors is understanding the notion of 'ordering vs ranking', the impact of context and many others.
  3. 3. Search Engine Results Page Search Engine Results Placement @bill_slawski & @BarbaraStarr
  4. 4.  Ranking search results based on entity metrics  Providing Knowledge Panels With Search Results  Maintaining search context  Near-duplicate filtering in search engine result page of an online shopping system  Clustered search results
  5. 5. “Returning, by one or more computing devices, an order list of results responsive to the query from the data store an online shopping system, filtered as a function of at least one of the distance and the cluster identifier.” Near-duplicate filtering in search engine result page of an online shopping system
  6. 6. “A plurality of metrics is determined associated with a search result obtained from a knowledge graph, wherein the metrics are indicative of the relevance of the search result, and the metrics are determined at least in part from the knowledge graph “ “Relates to ranking search results. Conventional techniques for ranking search results include alphabetical ordering and keyword matching “ “Results may be image thumbnail links, ordered horizontally based on score” Example metrics may be: Notable Entity Type Metric. Contribution Metric (and Fame Metric), Relatedness Metric, Prize Metric Ranking search results based on entity metrics
  7. 7. Notability, Notable type, Notable Type Metrics and more
  8. 8. US20130110825
  9. 9. Expertise in Entities Meta-Web patent mentions a “buy button” 10 times – it was a commercial startup @bill_slawski & @BarbaraStarr
  10. 10.  Automated online purchasing system  Meta-Web  Delegated authority evaluation system  User Contributed Knowledge Database  Graph Store  Knowledge web
  11. 11. Meta-Web Search Results US20040210602A1
  12. 12. “Generating a buy button with which the user can enter into a personalized purchase transaction to bring the user to a preferred vendor or list of vendors.” ”The search results page further includes one or more items that, when selected by the user lead to a product node for a particular product “ Meta-Web “the registry
  13. 13. US201450100569A1 US20150100569A1
  14. 14. In the knowledge web a community of people with knowledge to share put knowledge in the database using the user tools. The knowledge may be in the form of documents or other media, or it may be a descriptor of a book or other physical source Knowledge web
  15. 15. Brand Identifiers Entity Identifiers @bill_slawski & @BarbaraStarr
  16. 16.  Providing search results based on a compositional query  Crowdsourcing user-provided identifiers and associating them with brand identities
  17. 17. Entity Identifier WO2014089769A1
  18. 18. Brand Identifier US20140250192A1
  19. 19. “ In some implementations, search results include results identifying entity references. As used herein, an entity reference is an identifier, e.g., text, or other information that refers to an entity. For example, an entity may be the physical embodiment of George Washington, while an entity reference is an abstract concept that refers to George Washington. Where appropriate, based on context, it will be understood that the term entity as used herein may correspond to an entity reference, and the term entity reference as used herein may correspond to an entity. In some implementations, the search system may identify an entity type associated with an entity reference. The entity type may be a categorization or classification used to identify entity references in the data structure. For example, the entity reference "George Washington" may be associated with the entity types "U. S. President ,“ " Person, " and "Military Officer.”” Providing search results based on a compositional query
  20. 20. Different user-provided brand identifiers are extracted from messages provided by users of a social network. The identifiers are aggregated into two or more aggregate identi groups. When a brand identifier associated with a user request for content is determined to be in at least one of the aggregate ident groups, content items comprising one or more other brand identifiers of the at least one aggregate identity group are provided to the user. Crowdsourcing user-provided identifiers And associating them with brand identities
  21. 21. In Freebase? Internally, in the upcoming API @bill_slawski & @BarbaraStarr
  22. 22.  Meta-Web  Query Optimization  Providing Search Results based on a Compositional Query  Question answering using entity references in unstructured data
  23. 23. Query Optimization US20100121839A1
  24. 24. “entity references comprises ranking based on at least one ranking signal “ “an entity result is selected from the one or more entity references based at least in part on the ranking. An answer to the query is provided based at least in part on the entity result.” Question answering using entity references in unstructured data
  25. 25. “We describe the query optimization Techniques used by graphd, a schema-last, automatically indexed tuple-store which Supports freebase.com, a world-writable database. We demonstrate that the techniques described deliver performance that is generally comparable with traditional cost-based optimization techniques applied to the relational model.” Query Optimization
  26. 26. More Revenue (ads) Action - Entity Pairs @bill_slawski & @BarbaraStarr
  27. 27.  Entity-based searching with content selection  Providing a search results document that includes a user interface for performing an action in connection with a web page identified in the search results document  System and method for providing contextual actions on a search results page
  28. 28. US20140258014
  29. 29. “Annotation describing a user interface that is to be visually displayed in connection with information identifying the document when the information identifying the document is included in a search results document, the user interface including a user interface element that, when selected, causes an action to be performed in connection with the document” Providing a search results document that includes a user interface for performing an action in connection with a web page identified in the search results document
  30. 30. “Retrieving search results based in part on the search que identifying an entity-action pair comprising the named en and an online action associated with the entity; conducting a content auction for the entity-action pair based in part on auction bids received for the entity-actio pairs; selecting third-party content based on a result of th content auction” Entity-based searching with content selection
  31. 31. Standardize Entities Contexts Structure (IOT) @bill_slawski & @BarbaraStarr
  32. 32.  Apparatus and Method for Supplying Search Results with a Knowledge Card (Unpublished Google Provisional Patent)  Providing entity-specific content in response to a search query (Microsoft)  Entity detection and extraction for entity cards (Microsoft)  Providing entity-specific content in response to a search query (Microsoft)
  33. 33. US20120059838A1
  34. 34. https://www.seroundtable.com/google-mobile-color-lines-19898.html Mobile Card Interface
  35. 35. “Separate Templates may be used for separate fact entities. In the case of a person, the template may specify a description of the person and facts about the Person such as birthdate, birth location, career definitions, and the like.” Apparatus and Method for Supplying Search Results with a Knowledge Card (Unpublished Google Provisional Patent)
  36. 36. Templates/Cards are objects that know how to display (place) themselves Based on the device type. SERPS templates in this case are akin to “responsive design”
  37. 37. As displayed in Blended Search Engine Results pages (SERPS) Disparate data sources/sets mapped to distinct Search Engine Results Placements (SERPS) @bill_slawski & @BarbaraStarr
  38. 38.  Determination of a desired repository  Providing entity-specific content in response to a search query (Microsoft)  Interleaving search results  Browseable fact repository
  39. 39. Browseable Fact Repository US7774328B2
  40. 40. Universal Search Repositories US8266133B2
  41. 41. “A system receives a search query from a user and searches a group of repositories, based on the search query, to identify, for each of the repositories, a set of search results. The system also identifies one of the repositories based on a likelihood that the user desires information from the identified repository and presents the set of search results associated with the identified repository.” Determination of a desired repository
  42. 42. For Adwords Placement. Panda traffic For Data Quality @bill_slawski & @BarbaraStarr
  43. 43.  Classifying sites as low quality sites  Site quality score  Ranking search results  Predicting Site Quality  Providing a search results document that includes a user interface for performing an action in connection with a web page identified in the search results document  Focused Crawling for Structured Data (Paper)
  44. 44. “A link quality score is determined for the site using the number of resources in each resource quality group. If the link quality score is below a threshold link quality score, the site is classified as a low quality site.” Classifying sites as low quality sites
  45. 45. “In some implementations, the search system identifies data in a data structure that includes quality scores. Quality scores may be determined by global search history, extracting scores from external websites, search system developer input, user preferences, system settings, predetermined parameters, any other suitable technique, or any combination thereof. In an example, the search system retrieves movie review scores from a website such as IMDB. In another example, the search system may retrieve restaurant reviews from YELP and a newspaper. In some implementations, multiple quality scores associated with an entity are combined in a weighted or unweighted technique.” Ranking search results based on entity metrics
  46. 46. “We propose new methods of focused crawling specifically designed for collecting data-rich pages with greater efficiency. In particular, we propose a novel combination of online learning and bandit-based explore/exploit approaches to predict data-rich web pages based on the context of the page as well as using feedback from the extraction of metadata from previously seen pages. We show that these techniques significantly outperform state-of-the-art approaches for focused crawling, measured as the ratio of relevant pages and non-relevant pages collected within a given budget .” Focused Crawling For Structured Data
  47. 47. Bill Slawski, GoFishDigital, Director of Search Marketing Editor, SEO by the Sea https://plus.google.com/u/0/+BillSlawski https://twitter.com/bill_slawski https://www.linkedin.com/in/slawski Barbara Starr, Semantic Fuse, Managing Partner and Founder https://plus.google.com/u/0/+BarbaraStarr/ https://twitter.com/BarbaraStarr https://www.linkedin.com/in/barbarastarr

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