Using Context Vectors and Co-occurance in creating meaningful website content

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Keyword Research and
Topic Modeling in a Semantic Web
Presented by:
Bill Slawski
Director of SEO Research
Go Fish Digital

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Leo Carillo Rancho

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A historic renovated rancho

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Be Careful to Read All The Signs

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An Entity Audit Uncovers Surprises
Named entities are specific people, places, and things,
including products and brands.

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Paul Haahr- How Google Works

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Schema Markup, Google
MyBusiness Verification, Entry in
Wikipedia can lead to
Knowledge panels, but they are
only the start of adding entity
information…

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An elevator Ride from the DC Metro

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There is no clear sign telling people

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On the DC Metroline, you connect to:
• 91 Stations in Md, Va, & DC
• National Zoo
• 19 Smithsonian Museums
• National Gallery of Art
• Capital One Arena
• Fedex Field
• Pentagon City Shopping Mall

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Identify all Missing Entities

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Knowing how Google uses context and
semantically related phrases can improve the
content you create and how well you optimize
pages for particular queries.

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Keywords & Context Vectors
“For 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

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Look to Knowledge Bases
https://en.wikipedia.org/wiki/Horse

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For Other Meanings
https://en.wikipedia.org/wiki/Sawhorse

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See Disambiguation Pages
https://en.wikipedia.org/wiki/Vault_(gymnastics)

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Context Search Results
Context-based filtering of search results

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Map Keywords to Pages, then…
• Make sure you add words that indicate context
• Look up the top pages that rank for those keywords
• Find phrases that co-occur for that meaning
• See: Improving semantic topic clustering for search
Queries with word co-occurrence and biograph co-
clustering

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Phrase-Based Indexing
• Look for co-occurring phrases on pages that rank highly
for a query.
• Using these related phrases on a page can boost how it
ranks for that query (body hits)
• Using those related phrases as anchors can boost how
the page targeted ranks for that query (anchor hits)

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Related Words/Phrases
Thematic Modeling Using Related Words in Documents and Anchor Text

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Use Complete Phrases
• Incomplete Phrase… “President of the…”
• Complete Phrase… “President of the United States.”

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Use Meaningful Phrases
• Some phrases do not add meaning to a page:
Pay the Piper
Out of the Blue
Top of the Morning

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Predictive Aspects of Phrases
• 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.
• Integrated external related phrase information into a
phrase-based indexing information retrieval system

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Co-occurring Phrases/High Ranking Pages

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Clustered Meanings
• Jaguars- Cats, Cars, NFL Football Team
• Java – Programming Language, Island in Indonesia,
Drink
• Bank – A place to store money, a river’s side, to lean
to a side

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Ranking Documents Based on Contained Phrases (Body Hits)
“…a ranking stage in which the documents in the search
results are ranked, using the phrase information in each
document's 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 ‘body hits.’”
Integrated external related phrase information into a
phrase-based indexing information retrieval system

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Anchor Hits
”Sorting the documents on the outbound score
component makes documents that have many related
phrases to the query as ‘anchor hits,’ rank most highly,
thus representing these documents as ‘expert’
documents”
•Integrated external related phrase information into a
phrase-based indexing information retrieval system

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Personalization & Query Classifications
• 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

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Which Lincoln?

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Look at Knowledge Bases
• Abraham Lincoln

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Look at Top Search Results
• Lincoln, Towncar

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Look at Other Search Entities
• Lincoln, Nebraska

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Query Classifications
Search for “Lincoln” 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 “Lincoln.”
…determining whether to assign the classification to
the first query based upon classifications for the
identified search entities.
•Propagating query classifications

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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.

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Google Lens Schema
Smart Camera User Interface

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Further Reading
• Knowledge-Based Trust: Estimating the
Trustworthiness of Web Sources
• A Review of Relational Machine Learning for
Knowledge Graphs
• Knowledge Curation and Knowledge Fusion:
Challenges, Models, and Applications
• Improving semantic topic clustering for search queries
with word co-occurrence and bigraph co-clustering

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@bill_slawski
Questions? Ask Me At:
• Twitter: https://twitter.com/bill_slawski
• LinkedIn: https://www.linkedin.com/in/slawski/
• Facebook: https://www.facebook.com/bill.slawski
• Google+: https://plus.google.com/+BillSlawski
• SEO by the Sea: http://www.seobythesea.com/
• Go Fish Digital Blog: https://gofishdigital.com/blog/