keyword research and topic modeling in a semantic web
TRANSCRIPT
pubcon
bill_slawski
Keyword Research and
Topic Modeling in a Semantic Web
Presented by
Bill Slawski
Director of SEO Research
Go Fish Digital
pubcon
bill_slawski
Leo Carillo Rancho
pubcon
bill_slawski
A historic renovated rancho
pubcon
bill_slawski
Be Careful to Read All The Signs
pubcon
bill_slawski
An Entity Audit Uncovers Surprises
Named entities are specific people places and things
including products and brands
pubcon
bill_slawski
Paul Haahr- How Google Works
pubcon
bill_slawski
Schema Markup Google
MyBusiness Verification Entry in
Wikipedia can lead to
Knowledge panels but they are
only the start of adding entity
informationhellip
pubcon
bill_slawski
An elevator Ride from the DC Metro
pubcon
bill_slawski
There is no clear sign telling people
pubcon
bill_slawski
On the DC Metroline you connect to
bull 91 Stations in Md Va amp DC
bull National Zoo
bull 19 Smithsonian Museums
bull National Gallery of Art
bull Capital One Arena
bull Fedex Field
bull Pentagon City Shopping Mall
pubcon
bill_slawski
Identify all Missing Entities
pubcon
bill_slawski
Knowing how Google uses context and
semantically related phrases can improve the
content you create and how well you optimize
pages for particular queries
pubcon
bill_slawski
Keywords amp Context Vectors
ldquoFor example a horse to a rancher is an animal A horse to a carpenter is an implement of work A horse to a gymnast is an implement on which to perform certain exercises
User-context-based search engine
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Leo Carillo Rancho
pubcon
bill_slawski
A historic renovated rancho
pubcon
bill_slawski
Be Careful to Read All The Signs
pubcon
bill_slawski
An Entity Audit Uncovers Surprises
Named entities are specific people places and things
including products and brands
pubcon
bill_slawski
Paul Haahr- How Google Works
pubcon
bill_slawski
Schema Markup Google
MyBusiness Verification Entry in
Wikipedia can lead to
Knowledge panels but they are
only the start of adding entity
informationhellip
pubcon
bill_slawski
An elevator Ride from the DC Metro
pubcon
bill_slawski
There is no clear sign telling people
pubcon
bill_slawski
On the DC Metroline you connect to
bull 91 Stations in Md Va amp DC
bull National Zoo
bull 19 Smithsonian Museums
bull National Gallery of Art
bull Capital One Arena
bull Fedex Field
bull Pentagon City Shopping Mall
pubcon
bill_slawski
Identify all Missing Entities
pubcon
bill_slawski
Knowing how Google uses context and
semantically related phrases can improve the
content you create and how well you optimize
pages for particular queries
pubcon
bill_slawski
Keywords amp Context Vectors
ldquoFor example a horse to a rancher is an animal A horse to a carpenter is an implement of work A horse to a gymnast is an implement on which to perform certain exercises
User-context-based search engine
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
A historic renovated rancho
pubcon
bill_slawski
Be Careful to Read All The Signs
pubcon
bill_slawski
An Entity Audit Uncovers Surprises
Named entities are specific people places and things
including products and brands
pubcon
bill_slawski
Paul Haahr- How Google Works
pubcon
bill_slawski
Schema Markup Google
MyBusiness Verification Entry in
Wikipedia can lead to
Knowledge panels but they are
only the start of adding entity
informationhellip
pubcon
bill_slawski
An elevator Ride from the DC Metro
pubcon
bill_slawski
There is no clear sign telling people
pubcon
bill_slawski
On the DC Metroline you connect to
bull 91 Stations in Md Va amp DC
bull National Zoo
bull 19 Smithsonian Museums
bull National Gallery of Art
bull Capital One Arena
bull Fedex Field
bull Pentagon City Shopping Mall
pubcon
bill_slawski
Identify all Missing Entities
pubcon
bill_slawski
Knowing how Google uses context and
semantically related phrases can improve the
content you create and how well you optimize
pages for particular queries
pubcon
bill_slawski
Keywords amp Context Vectors
ldquoFor example a horse to a rancher is an animal A horse to a carpenter is an implement of work A horse to a gymnast is an implement on which to perform certain exercises
User-context-based search engine
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Be Careful to Read All The Signs
pubcon
bill_slawski
An Entity Audit Uncovers Surprises
Named entities are specific people places and things
including products and brands
pubcon
bill_slawski
Paul Haahr- How Google Works
pubcon
bill_slawski
Schema Markup Google
MyBusiness Verification Entry in
Wikipedia can lead to
Knowledge panels but they are
only the start of adding entity
informationhellip
pubcon
bill_slawski
An elevator Ride from the DC Metro
pubcon
bill_slawski
There is no clear sign telling people
pubcon
bill_slawski
On the DC Metroline you connect to
bull 91 Stations in Md Va amp DC
bull National Zoo
bull 19 Smithsonian Museums
bull National Gallery of Art
bull Capital One Arena
bull Fedex Field
bull Pentagon City Shopping Mall
pubcon
bill_slawski
Identify all Missing Entities
pubcon
bill_slawski
Knowing how Google uses context and
semantically related phrases can improve the
content you create and how well you optimize
pages for particular queries
pubcon
bill_slawski
Keywords amp Context Vectors
ldquoFor example a horse to a rancher is an animal A horse to a carpenter is an implement of work A horse to a gymnast is an implement on which to perform certain exercises
User-context-based search engine
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
An Entity Audit Uncovers Surprises
Named entities are specific people places and things
including products and brands
pubcon
bill_slawski
Paul Haahr- How Google Works
pubcon
bill_slawski
Schema Markup Google
MyBusiness Verification Entry in
Wikipedia can lead to
Knowledge panels but they are
only the start of adding entity
informationhellip
pubcon
bill_slawski
An elevator Ride from the DC Metro
pubcon
bill_slawski
There is no clear sign telling people
pubcon
bill_slawski
On the DC Metroline you connect to
bull 91 Stations in Md Va amp DC
bull National Zoo
bull 19 Smithsonian Museums
bull National Gallery of Art
bull Capital One Arena
bull Fedex Field
bull Pentagon City Shopping Mall
pubcon
bill_slawski
Identify all Missing Entities
pubcon
bill_slawski
Knowing how Google uses context and
semantically related phrases can improve the
content you create and how well you optimize
pages for particular queries
pubcon
bill_slawski
Keywords amp Context Vectors
ldquoFor example a horse to a rancher is an animal A horse to a carpenter is an implement of work A horse to a gymnast is an implement on which to perform certain exercises
User-context-based search engine
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Paul Haahr- How Google Works
pubcon
bill_slawski
Schema Markup Google
MyBusiness Verification Entry in
Wikipedia can lead to
Knowledge panels but they are
only the start of adding entity
informationhellip
pubcon
bill_slawski
An elevator Ride from the DC Metro
pubcon
bill_slawski
There is no clear sign telling people
pubcon
bill_slawski
On the DC Metroline you connect to
bull 91 Stations in Md Va amp DC
bull National Zoo
bull 19 Smithsonian Museums
bull National Gallery of Art
bull Capital One Arena
bull Fedex Field
bull Pentagon City Shopping Mall
pubcon
bill_slawski
Identify all Missing Entities
pubcon
bill_slawski
Knowing how Google uses context and
semantically related phrases can improve the
content you create and how well you optimize
pages for particular queries
pubcon
bill_slawski
Keywords amp Context Vectors
ldquoFor example a horse to a rancher is an animal A horse to a carpenter is an implement of work A horse to a gymnast is an implement on which to perform certain exercises
User-context-based search engine
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Schema Markup Google
MyBusiness Verification Entry in
Wikipedia can lead to
Knowledge panels but they are
only the start of adding entity
informationhellip
pubcon
bill_slawski
An elevator Ride from the DC Metro
pubcon
bill_slawski
There is no clear sign telling people
pubcon
bill_slawski
On the DC Metroline you connect to
bull 91 Stations in Md Va amp DC
bull National Zoo
bull 19 Smithsonian Museums
bull National Gallery of Art
bull Capital One Arena
bull Fedex Field
bull Pentagon City Shopping Mall
pubcon
bill_slawski
Identify all Missing Entities
pubcon
bill_slawski
Knowing how Google uses context and
semantically related phrases can improve the
content you create and how well you optimize
pages for particular queries
pubcon
bill_slawski
Keywords amp Context Vectors
ldquoFor example a horse to a rancher is an animal A horse to a carpenter is an implement of work A horse to a gymnast is an implement on which to perform certain exercises
User-context-based search engine
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
An elevator Ride from the DC Metro
pubcon
bill_slawski
There is no clear sign telling people
pubcon
bill_slawski
On the DC Metroline you connect to
bull 91 Stations in Md Va amp DC
bull National Zoo
bull 19 Smithsonian Museums
bull National Gallery of Art
bull Capital One Arena
bull Fedex Field
bull Pentagon City Shopping Mall
pubcon
bill_slawski
Identify all Missing Entities
pubcon
bill_slawski
Knowing how Google uses context and
semantically related phrases can improve the
content you create and how well you optimize
pages for particular queries
pubcon
bill_slawski
Keywords amp Context Vectors
ldquoFor example a horse to a rancher is an animal A horse to a carpenter is an implement of work A horse to a gymnast is an implement on which to perform certain exercises
User-context-based search engine
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
There is no clear sign telling people
pubcon
bill_slawski
On the DC Metroline you connect to
bull 91 Stations in Md Va amp DC
bull National Zoo
bull 19 Smithsonian Museums
bull National Gallery of Art
bull Capital One Arena
bull Fedex Field
bull Pentagon City Shopping Mall
pubcon
bill_slawski
Identify all Missing Entities
pubcon
bill_slawski
Knowing how Google uses context and
semantically related phrases can improve the
content you create and how well you optimize
pages for particular queries
pubcon
bill_slawski
Keywords amp Context Vectors
ldquoFor example a horse to a rancher is an animal A horse to a carpenter is an implement of work A horse to a gymnast is an implement on which to perform certain exercises
User-context-based search engine
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
On the DC Metroline you connect to
bull 91 Stations in Md Va amp DC
bull National Zoo
bull 19 Smithsonian Museums
bull National Gallery of Art
bull Capital One Arena
bull Fedex Field
bull Pentagon City Shopping Mall
pubcon
bill_slawski
Identify all Missing Entities
pubcon
bill_slawski
Knowing how Google uses context and
semantically related phrases can improve the
content you create and how well you optimize
pages for particular queries
pubcon
bill_slawski
Keywords amp Context Vectors
ldquoFor example a horse to a rancher is an animal A horse to a carpenter is an implement of work A horse to a gymnast is an implement on which to perform certain exercises
User-context-based search engine
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Identify all Missing Entities
pubcon
bill_slawski
Knowing how Google uses context and
semantically related phrases can improve the
content you create and how well you optimize
pages for particular queries
pubcon
bill_slawski
Keywords amp Context Vectors
ldquoFor example a horse to a rancher is an animal A horse to a carpenter is an implement of work A horse to a gymnast is an implement on which to perform certain exercises
User-context-based search engine
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Knowing how Google uses context and
semantically related phrases can improve the
content you create and how well you optimize
pages for particular queries
pubcon
bill_slawski
Keywords amp Context Vectors
ldquoFor example a horse to a rancher is an animal A horse to a carpenter is an implement of work A horse to a gymnast is an implement on which to perform certain exercises
User-context-based search engine
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Keywords amp Context Vectors
ldquoFor example a horse to a rancher is an animal A horse to a carpenter is an implement of work A horse to a gymnast is an implement on which to perform certain exercises
User-context-based search engine
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Look to Knowledge Bases
httpsenwikipediaorgwikiHorse
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
For Other Meanings
httpsenwikipediaorgwikiSawhorse
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
See Disambiguation Pages
httpsenwikipediaorgwikiVault_(gymnastics)
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Context Search ResultsContext-based filtering of search results
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Map Keywords to Pages thenhellip
bull Make sure you add words that indicate context
bull Look up the top pages that rank for those keywords
bull Find phrases that co-occur for that meaning
bull See Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Phrase-Based Indexingbull Look for co-occurring phrases on pages that rank highly
for a query
bull Using these related phrases on a page can boost how it ranks for that query (body hits)
bull Using those related phrases as anchors can boost how the page targeted ranks for that query (anchor hits)
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Related WordsPhrasesThematic Modeling Using Related Words in Documents and Anchor Text
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Use Complete Phrases
bull Incomplete Phrasehellip ldquoPresident of thehelliprdquo
bull Complete Phrasehellip ldquoPresident of the United Statesrdquo
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Use Meaningful Phrases
bull Some phrases do not add meaning to a page
Pay the Piper
Out of the Blue
Top of the Morning
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Predictive Aspects of Phrasesbull Semantically related phrases will be those that are
commonly used to discuss or describe a given topic or concept such as President of the United States and White House For a given phrase the related phrases can be ordered according to their relevance or significance based on their respective prediction measures
bull Integrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Co-occurring PhrasesHigh Ranking Pages
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Clustered Meanings
bull Jaguars- Cats Cars NFL Football Team
bull Java ndash Programming Language Island in Indonesia
Drink
bull Bank ndash A place to store money a riverrsquos side to lean
to a side
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Ranking Documents Based on Contained Phrases (Body Hits)
ldquohellipa ranking stage in which the documents in the search
results are ranked using the phrase information in each
documents related phrase bit vector and the cluster
bit vector for the query phrases This approach ranks
documents according to the phrases that are contained
in the document or informally lsquobody hitsrsquordquo
Integrated external related phrase information into a
phrase-based indexing information retrieval system
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Anchor Hits
rdquoSorting the documents on the outbound score component makes documents that have many related phrases to the query as lsquoanchor hitsrsquo rank most highly thus representing these documents as lsquoexpertrsquo documentsrdquo
bullIntegrated external related phrase information into a phrase-based indexing information retrieval system
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Personalization amp Query Classifications
bull Depending upon results selected by a searcher the
results they see may fall into a specific category from
a biased document set
Personalizing Search Results at Google
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Which Lincoln
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Look at Knowledge Bases
bull Abraham Lincoln
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Look at Top Search Results
bull Lincoln Towncar
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Look at Other Search Entities
bull Lincoln Nebraska
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Query Classifications
Search for ldquoLincolnrdquo and click on the Person (Abe) the Place (Nebraska) or the thing (towncar) What you click on may determine what you see in the future on searches for ldquoLincolnrdquo
hellipdetermining whether to assign the classification to the first query based upon classifications for the identified search entities
bullPropagating query classifications
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Searches are what we type and what we say but
they will also be based upon what we see and
take photos of in the future
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Google Lens Schema
Smart Camera User Interface
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Further Readingbull Knowledge-Based Trust Estimating the
Trustworthiness of Web Sources
bull A Review of Relational Machine Learning for Knowledge Graphs
bull Knowledge Curation and Knowledge Fusion Challenges Models and Applications
bull Improving semantic topic clustering for search queries with word co-occurrence and bigraph co-clustering
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog
pubcon
bill_slawski
Questions Ask Me At
bull Twitter httpstwittercombill_slawski
bull LinkedIn httpswwwlinkedincominslawski
bull Facebook httpswwwfacebookcombillslawski
bull Google+ httpsplusgooglecom+BillSlawski
bull SEO by the Sea httpwwwseobytheseacom
bull Go Fish Digital Blog httpsgofishdigitalcomblog