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June 23, 2015 Semantic Web Meetup, SEO San Diego Meetup, SEM San Diego Meetup
Courtyard San Diego Old Town
#SEOSDM
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.
Search Engine Results Page
Search Engine Results Placement
@bill_slawski &
@BarbaraStarr
 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
“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
“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
Notability, Notable type, Notable Type Metrics and more
US20130110825
Expertise in Entities
Meta-Web patent mentions a “buy button”
10 times – it was a commercial startup
@bill_slawski & @BarbaraStarr
 Automated online purchasing system
 Meta-Web
 Delegated authority evaluation system
 User Contributed Knowledge Database
 Graph Store
 Knowledge web
Meta-Web Search Results
US20040210602A1
“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
US201450100569A1
US20150100569A1
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
Brand Identifiers
Entity Identifiers
@bill_slawski & @BarbaraStarr
 Providing search results based on a
compositional query
 Crowdsourcing user-provided identifiers and
associating them with brand identities
Entity Identifier
WO2014089769A1
Brand Identifier
US20140250192A1
“ 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
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
In Freebase?
Internally, in the upcoming API
@bill_slawski & @BarbaraStarr
 Meta-Web
 Query Optimization
 Providing Search Results based on a
Compositional Query
 Question answering using entity references in
unstructured data
Query Optimization
US20100121839A1
“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
“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
More Revenue (ads)
Action - Entity Pairs
@bill_slawski & @BarbaraStarr
 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
US20140258014
“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
“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
Standardize Entities
Contexts Structure (IOT)
@bill_slawski & @BarbaraStarr
 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)
US20120059838A1
https://www.seroundtable.com/google-mobile-color-lines-19898.html
Mobile Card Interface
“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)
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”
As displayed in Blended Search
Engine Results pages (SERPS)
Disparate data sources/sets
mapped to distinct Search Engine
Results Placements (SERPS)
@bill_slawski & @BarbaraStarr
 Determination of a desired repository
 Providing entity-specific content in response to
a search query (Microsoft)
 Interleaving search results
 Browseable fact repository
Browseable Fact Repository
US7774328B2
Universal Search Repositories
US8266133B2
“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
For Adwords Placement. Panda traffic
For Data Quality
@bill_slawski & @BarbaraStarr
 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)
“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
“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
“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
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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Ranking in Google Since The Advent of The Knowledge Graph

  • 1. June 23, 2015 Semantic Web Meetup, SEO San Diego Meetup, SEM San Diego Meetup Courtyard San Diego Old Town #SEOSDM
  • 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.
  • 4. Search Engine Results Page Search Engine Results Placement @bill_slawski & @BarbaraStarr
  • 5.  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
  • 6. “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
  • 7. “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
  • 8. Notability, Notable type, Notable Type Metrics and more
  • 9.
  • 11. Expertise in Entities Meta-Web patent mentions a “buy button” 10 times – it was a commercial startup @bill_slawski & @BarbaraStarr
  • 12.  Automated online purchasing system  Meta-Web  Delegated authority evaluation system  User Contributed Knowledge Database  Graph Store  Knowledge web
  • 14. “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
  • 16. 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
  • 18.  Providing search results based on a compositional query  Crowdsourcing user-provided identifiers and associating them with brand identities
  • 21. “ 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
  • 22. 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
  • 23. In Freebase? Internally, in the upcoming API @bill_slawski & @BarbaraStarr
  • 24.  Meta-Web  Query Optimization  Providing Search Results based on a Compositional Query  Question answering using entity references in unstructured data
  • 26.
  • 27.
  • 28. “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
  • 29. “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
  • 30. More Revenue (ads) Action - Entity Pairs @bill_slawski & @BarbaraStarr
  • 31.  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
  • 33.
  • 34. “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
  • 35. “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
  • 36. Standardize Entities Contexts Structure (IOT) @bill_slawski & @BarbaraStarr
  • 37.  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)
  • 40. “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)
  • 41. 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”
  • 42. As displayed in Blended Search Engine Results pages (SERPS) Disparate data sources/sets mapped to distinct Search Engine Results Placements (SERPS) @bill_slawski & @BarbaraStarr
  • 43.  Determination of a desired repository  Providing entity-specific content in response to a search query (Microsoft)  Interleaving search results  Browseable fact repository
  • 46. “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
  • 47. For Adwords Placement. Panda traffic For Data Quality @bill_slawski & @BarbaraStarr
  • 48.  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)
  • 49. “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
  • 50. “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
  • 51. “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
  • 52. 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