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The real reason why
Google Hummingbird exists
Friday, 22nd April 2016
Introduction
Gerald Murphy
Sections of this presentation
The real reason why
Google Hummingbird exists
Semantic search
Mobile
Future of search
Keywords
Why does Google Hummingbird exist?
Usability principles
User's
language
Consistency
Minization of
user memory
load
Flexibility
and efficiency
of user
Aesthetic and
minimalist
design
Chunking
Progressive
levels of
detail
Navigational
feedback
Evolution of search
1945
Where? Why?
1950/60’s
1991
What is a search engine?Definitionone
Utility tool
used to locate
web sites on
the web Definitiontwo
A searcher
driven
program
offering
unique
features to
build and find
information
Definitionthree
Most popular
method of
finding
information
One of the most demanding
challenges for modern search
engines is understanding
search intent
Copyright free image
Truncation example
Swimming
Swimwear
swimmers
• It is Google’s algorithm
“the whole shebang”
• Came out in August 2013
• Upgrade of: index
stemming (truncation by
the user) and synonyms
• Tries to understand
keyword intent
Swim* =
Are search engines semantic?
.
Semantic search
Are search engines semantic?
.
Semantic search
Health
certified
Strict
babysitter
Knows how
to work
Xbox
Available
on
Thursday
Within
price range
Types of
semantic search
engines
State of the art
Form-
based
engines
RDF-
based
querying
languages
Question-
answering
tools
Semantic-based
keyword search
Semantic search
Types of semantic search engines
• One of the first form-based engines was called Simple HTML Ontology
Extensions (SHOE)
Form-based
Types of semantic search engines
• Practical implementation
Form-based
https://www.google.com/patents/US20130226953
Types of semantic search engines
• Resource Description Framework Schema -- RDF(S)
• This is what powers Knowledge Graph and Bases
› Proper term for Google Knowledge Graph is Conceptual Graphs
• Uses command to enhance results, such as:
› isInterestedBy
› hasForWorkInterest
› hasForPersonalInterest
RDF-based querying languages
Closely linked
with location
Length
Breadth
Height
Why did the major engines sign up to Schema?
To get a head start on semantic…voluntarily tell the engine
Types of semantic search engines
• Looks at string distance metric, generic lexical resources, such as WordNet
plus structure of the input ontology
• Heavily replies on Machine Learning (which promoted Google RankBrain)
Question-answering tools
Types of semantic search engines
• Joining the dots, then learning from them
• Distributed extensibility is a very important part of semantic search
Semantic-based keyword search
Knowledge Graph and Bases
• Presenting media objects to the user using an additional SERP snippet or
two
› Encouraging the searcher to stay on the SERP
• Knowledge Graphs and Bases link up media objects:
› E.g. webpages, images, audio clips, social media profiles
• AI is helping entities go in the right direction
Google Hummingbird…
• Google Hummingbird is about enhancing synonyms
› It is not semantic search
• It is a new engine of a car or new wheels of a bike, if you like, in order to
improve performance (speed and precision)
• It is helping to improve Google Knowledge Graph
…as it stands
Concepts familiar to the user
How do I get to the nearest restaurant?
Semantic search
Keyword filtering1
Required
& optional keyword
2
Subject keyword is
semantically searched
3
User profile
Geo
Concepts familiar to the user
How do I get to the nearest restaurant?
User profile
Real semantic search
By means
of
By what
method
By whose
help
From what
source
Synonyms = what Google Hummingbird is currently working on
Keywords
Keywords
Environment
Gender
Age Location
Culture
Linguistics
Voice
search
Use of words and phrases
Keywords
Use of words and phrases
iPhone
Mobile technology
explosion
1 or 2 words?
What the hell
happened?
Voice search is going to diminish the
searcher’s use of incorrect keywords
and therefore impact exact phrase
keywords
Categorising search queries
• When we search we often think of things we do not type
Samsung Galaxy Edge 7
Cognition
subjective reviews on
comprehensive information
Exhaustive Comprehensible Objective
Subjective Concrete Abstract
We often think
with these
keywords but
we do not type
them
Semantically, renaming search queries
Direct
[subjective 5 star
hotel in Miami]
Transformed
[review latest
blackberry handset]
None
[blackberry]
No more informational, navigational, transactional, connectivity
0
0.5
1
1.5
2
2.5
3
3.5
Nouns (unique) Nouns
(overlapping)
Verbs Adjectives
Difference between typed and voice
search queries and their component
words
Typed queries
Voice search
Transactional Informational
Mobile
Mobiles
• In 1991, Mark Weiser, a US scientist, coined the terms:
› Inch-sized computer
› Mobile
› Foot-sized computer
› Tablet
› Yard-sized computer
› Web-enabled TV
Origin
Organisation for Economic Co-operation and Development
said in June 2010 mobile had:
1.5 times fixed broadband
Mobiles
• Most personal and confidential piece of technological device
• Used in active or personal contexts and activities in a natural and dynamic
way
• Used in a variety of situations:
Today
Rush
While
commuting
To fill idle
time
While
queuing
At home
Comfortably
sitting on the
sofa
What we know about mobile
Lots of things, or is it?
Visually the same
as desktop
Size of screen
does not matter
Social activity
Reduces button
tapping accuracy
by 30%
Clutter free
High-end mobiles
are similar to
desktop searchers
Good
abandonment is
higher
~70% of searches
in work or at
home
Mostly static
searches
Under-
researched
area
Mobile has come a long way!
• Blood pressure monitors
• Skin conduction
• Respiration sensors
In 2001
81% accuracy
Boredom and mobile interaction
.
More indicative
of boredom
Switching phone on
Changing screen orientation
Not so
indicative
Social network notifications
Frequency of open notification centre
Change screen status
App launches
Charging time
Amount of transmitted data
Activesearch
forstimuli
Analysing if we are bored
Google Now
• There:
› to support task continuation
› help bored searchers
Purpose for mobile search
Boredom state
I don’t like,
I’m bored
Boredom trait
I used to
like, but
now I’m
bored
Boredom and search
• Mobile has an ephemeral nature
.
Evoked by an urgent
need
When is the next bus home?
Location-based filtering
Semantic tools
Boredom and search
• Mobile has an ephemeral nature
.
Triggered by desire to
fill idle time
Funniest cinema movies
Mobile-tailored content
Social tagging
Boredom and search
• Mobile has an ephemeral nature
.
Prompted by an
event, situation, no
need to fill
Which cinema shows the film I just
seen the announcement about?
Ephemeral need, response is not yet
needed
The future
Linking technology with the searcher
Link-less
world
Mobile
Conversational
search
Artificial
Intelligence
Sweat glands
Technology
Predictive
search
Semantics
Internet of
Things
Semantics,
technology and
mobile
Snippet length
Query type,
mobile
References
• Kato, M.P., Yamaoto, T., Ohshima, H., and Tanaka, K. Cognitive search intents hidden behind queries: A
user study on query formulations. WWW ‘14 Companion
• Schilit, B. N. Mobile computing: Looking to the future. Computer.
• Gomez-Barroso, J. L. Factors required for mobile search going mainstream. Online Information Review.
36(6)
• Xu, Z., Luo, X., Yu, J., and Xu, W. Measuring semantic similarity between words by removing noise and
redundancy in web snippets. John Wiley & Sons Ltd.
• Matic, A., Pielot, M. and Oliver, N. Boredom-computer interaction: Boredom proneness and the use of
smartphone.
• Lane, N., Lymberopoulos, D., Zhao, F., Campbell, A. T. Hapori: Context-based local search for mobile
phones using community behavioural modeling and similarity. Ubicomp.
• Goldsten, J., Kantrowitz, M., Mittal, V., and Carbonell, J. Summarising text documents: Sentence and
evaluation metrics.
• Crossan, A., Murray-Smith, R., Brewser, S., Kelly, J., Musizza, B. Kelly, S.Gait phase effects in mobile
interaction. CHI 2005.
• Vang, K. J. Ethics of Google’s Knowledge Graph: Some considerations. Journal of Information,
Communication and Ethics in Society.
• Pielot, M. Dingler, T. Pedro, J. S., and Oliver, N. When attention is not scarce – detecting boredom from
mobile phone usage. Ubicomp.
• Kamvar, M. and Baluja, S. A large scale study of wireless search behavior: Google mobile search. CHI.
• Lei, Y., Uren, V., Motta, E. SemSearch: A search engine for the semantic web.
Key readings
Summary
Search
originated in
1945
Hummingbird
is the actual
algorithm
Form-based
RDF-based
querying
languages
Question-
answering
Semantic-
based
Keywords and
voice search
Direct Transformed
None
Mobile and
boredom
Google Now Future
Thank you!

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The real reason Google Hummingbird exists (brightonSEO, Friday 22nd April 2016)

  • 1. The real reason why Google Hummingbird exists Friday, 22nd April 2016
  • 3. Sections of this presentation The real reason why Google Hummingbird exists Semantic search Mobile Future of search Keywords
  • 4. Why does Google Hummingbird exist? Usability principles User's language Consistency Minization of user memory load Flexibility and efficiency of user Aesthetic and minimalist design Chunking Progressive levels of detail Navigational feedback
  • 5. Evolution of search 1945 Where? Why? 1950/60’s 1991
  • 6. What is a search engine?Definitionone Utility tool used to locate web sites on the web Definitiontwo A searcher driven program offering unique features to build and find information Definitionthree Most popular method of finding information
  • 7. One of the most demanding challenges for modern search engines is understanding search intent
  • 8. Copyright free image Truncation example Swimming Swimwear swimmers • It is Google’s algorithm “the whole shebang” • Came out in August 2013 • Upgrade of: index stemming (truncation by the user) and synonyms • Tries to understand keyword intent Swim* =
  • 9. Are search engines semantic? . Semantic search
  • 10. Are search engines semantic? . Semantic search Health certified Strict babysitter Knows how to work Xbox Available on Thursday Within price range
  • 12. State of the art Form- based engines RDF- based querying languages Question- answering tools Semantic-based keyword search Semantic search
  • 13. Types of semantic search engines • One of the first form-based engines was called Simple HTML Ontology Extensions (SHOE) Form-based
  • 14. Types of semantic search engines • Practical implementation Form-based https://www.google.com/patents/US20130226953
  • 15. Types of semantic search engines • Resource Description Framework Schema -- RDF(S) • This is what powers Knowledge Graph and Bases › Proper term for Google Knowledge Graph is Conceptual Graphs • Uses command to enhance results, such as: › isInterestedBy › hasForWorkInterest › hasForPersonalInterest RDF-based querying languages Closely linked with location Length Breadth Height
  • 16. Why did the major engines sign up to Schema? To get a head start on semantic…voluntarily tell the engine
  • 17. Types of semantic search engines • Looks at string distance metric, generic lexical resources, such as WordNet plus structure of the input ontology • Heavily replies on Machine Learning (which promoted Google RankBrain) Question-answering tools
  • 18. Types of semantic search engines • Joining the dots, then learning from them • Distributed extensibility is a very important part of semantic search Semantic-based keyword search
  • 19. Knowledge Graph and Bases • Presenting media objects to the user using an additional SERP snippet or two › Encouraging the searcher to stay on the SERP • Knowledge Graphs and Bases link up media objects: › E.g. webpages, images, audio clips, social media profiles • AI is helping entities go in the right direction
  • 20. Google Hummingbird… • Google Hummingbird is about enhancing synonyms › It is not semantic search • It is a new engine of a car or new wheels of a bike, if you like, in order to improve performance (speed and precision) • It is helping to improve Google Knowledge Graph …as it stands
  • 21. Concepts familiar to the user How do I get to the nearest restaurant? Semantic search Keyword filtering1 Required & optional keyword 2 Subject keyword is semantically searched 3 User profile
  • 22. Geo Concepts familiar to the user How do I get to the nearest restaurant? User profile Real semantic search By means of By what method By whose help From what source Synonyms = what Google Hummingbird is currently working on
  • 25. Keywords Use of words and phrases iPhone Mobile technology explosion 1 or 2 words? What the hell happened?
  • 26. Voice search is going to diminish the searcher’s use of incorrect keywords and therefore impact exact phrase keywords
  • 27. Categorising search queries • When we search we often think of things we do not type Samsung Galaxy Edge 7 Cognition subjective reviews on comprehensive information Exhaustive Comprehensible Objective Subjective Concrete Abstract We often think with these keywords but we do not type them
  • 28. Semantically, renaming search queries Direct [subjective 5 star hotel in Miami] Transformed [review latest blackberry handset] None [blackberry] No more informational, navigational, transactional, connectivity 0 0.5 1 1.5 2 2.5 3 3.5 Nouns (unique) Nouns (overlapping) Verbs Adjectives Difference between typed and voice search queries and their component words Typed queries Voice search Transactional Informational
  • 30. Mobiles • In 1991, Mark Weiser, a US scientist, coined the terms: › Inch-sized computer › Mobile › Foot-sized computer › Tablet › Yard-sized computer › Web-enabled TV Origin Organisation for Economic Co-operation and Development said in June 2010 mobile had: 1.5 times fixed broadband
  • 31. Mobiles • Most personal and confidential piece of technological device • Used in active or personal contexts and activities in a natural and dynamic way • Used in a variety of situations: Today Rush While commuting To fill idle time While queuing At home Comfortably sitting on the sofa
  • 32. What we know about mobile Lots of things, or is it? Visually the same as desktop Size of screen does not matter Social activity Reduces button tapping accuracy by 30% Clutter free High-end mobiles are similar to desktop searchers Good abandonment is higher ~70% of searches in work or at home Mostly static searches Under- researched area
  • 33. Mobile has come a long way! • Blood pressure monitors • Skin conduction • Respiration sensors In 2001 81% accuracy
  • 34. Boredom and mobile interaction . More indicative of boredom Switching phone on Changing screen orientation Not so indicative Social network notifications Frequency of open notification centre Change screen status App launches Charging time Amount of transmitted data Activesearch forstimuli Analysing if we are bored
  • 35. Google Now • There: › to support task continuation › help bored searchers Purpose for mobile search Boredom state I don’t like, I’m bored Boredom trait I used to like, but now I’m bored
  • 36. Boredom and search • Mobile has an ephemeral nature . Evoked by an urgent need When is the next bus home? Location-based filtering Semantic tools
  • 37. Boredom and search • Mobile has an ephemeral nature . Triggered by desire to fill idle time Funniest cinema movies Mobile-tailored content Social tagging
  • 38. Boredom and search • Mobile has an ephemeral nature . Prompted by an event, situation, no need to fill Which cinema shows the film I just seen the announcement about? Ephemeral need, response is not yet needed
  • 39. The future Linking technology with the searcher Link-less world Mobile Conversational search Artificial Intelligence Sweat glands Technology Predictive search Semantics Internet of Things Semantics, technology and mobile Snippet length Query type, mobile
  • 40. References • Kato, M.P., Yamaoto, T., Ohshima, H., and Tanaka, K. Cognitive search intents hidden behind queries: A user study on query formulations. WWW ‘14 Companion • Schilit, B. N. Mobile computing: Looking to the future. Computer. • Gomez-Barroso, J. L. Factors required for mobile search going mainstream. Online Information Review. 36(6) • Xu, Z., Luo, X., Yu, J., and Xu, W. Measuring semantic similarity between words by removing noise and redundancy in web snippets. John Wiley & Sons Ltd. • Matic, A., Pielot, M. and Oliver, N. Boredom-computer interaction: Boredom proneness and the use of smartphone. • Lane, N., Lymberopoulos, D., Zhao, F., Campbell, A. T. Hapori: Context-based local search for mobile phones using community behavioural modeling and similarity. Ubicomp. • Goldsten, J., Kantrowitz, M., Mittal, V., and Carbonell, J. Summarising text documents: Sentence and evaluation metrics. • Crossan, A., Murray-Smith, R., Brewser, S., Kelly, J., Musizza, B. Kelly, S.Gait phase effects in mobile interaction. CHI 2005. • Vang, K. J. Ethics of Google’s Knowledge Graph: Some considerations. Journal of Information, Communication and Ethics in Society. • Pielot, M. Dingler, T. Pedro, J. S., and Oliver, N. When attention is not scarce – detecting boredom from mobile phone usage. Ubicomp. • Kamvar, M. and Baluja, S. A large scale study of wireless search behavior: Google mobile search. CHI. • Lei, Y., Uren, V., Motta, E. SemSearch: A search engine for the semantic web. Key readings
  • 41. Summary Search originated in 1945 Hummingbird is the actual algorithm Form-based RDF-based querying languages Question- answering Semantic- based Keywords and voice search Direct Transformed None Mobile and boredom Google Now Future

Hinweis der Redaktion

  1. Hello, thanks for staying. I’m dying for another drink too…
  2. My name is Gerald Murphy. I’m a paid Associate Lecturer of information retrieval in one of the UK’s oldest search departments. I also write for the long-standing search engine marketing website in the world, Search Engine Watch And I work with the coolest brands in BrightEdge, singlehandedly the best integrated SEO platform since we have partnerships with MajesticSEO, Facebook and Twitter As you can tell from the background picture, and my beautiful accent, I’m a Belfast boy. The city that built the unsinkable ship that ended up sinking
  3. Rather than do a boring presentation on just semantic search, I’ve split today’s presentation into 4 parts: Semantic search Keywords Mobile Future of search
  4. We’ll do this by weaving in solid understanding from the 8 usability principles
  5. V. Bush Automated bots Web inception Engines to organize mess
  6. Loads of definitions of web search…
  7. But, one thing we can agree on is… …[read]
  8. Google Hummingbird is here to help to understand KW intent It’s not a ranking factor, it’s the actual algorithm It came out over 2 and a half years ago
  9. Babysitting anology
  10. There are 4 types, let’s take a look
  11. The first is SHOE Think forms and databases And think Schema
  12. Google filed this Patent before they announced Hummingbird. It splits the KW up into forms, but the index needs to be split and semantically linked. Something Google is unlikely to have
  13. Second, RDF This is what powers Google Knowledge Graph Proper term is conception graphs Unlike SHOE, it uses commands via categories Closely linked to location and personalization
  14. Both SHOE and RDF rely on additional code, basically Schema, it is no wonder why all the major engines have agreed on Schema code
  15. Question-answering tools, are essentially several dictionaries and encyclopedia Think about to your time at school. You were given a reading list of various books. Each book had additional and enhancement information in it. This is what question-answering semantics is like. Since we had to go and verify if our knowledge was correct, just as a uni test would do, AI is used to replace the exam/test
  16. Fourthly, and finally, there is an array of information on the existing web and semantic-based keyword search. Engines scrape this info and learn from references or citations. Once a KW is searched, it is matched off these citations to form a semantic search.
  17. KG is not semantic search. It is there to link media objects, which web search engines have always been able to do. It is also there to keep us on the engine for longer. It’s relevant and sub consciously reinforces quality information.
  18. So, Hummingbird is all about enhancing synonyms. It’s not semantic search. Ask and engine for a babysitter or get a native speaker to play around with Translate, it’s a mess
  19. Search will slowly move towards semantic search filtering. Here’s a simplified version: Long tail keyword is entered Engine splits it into 2, required and optional Subject KW is semantically searched
  20. 7 factors influence our choice of KWs For semantics, 2 are particularly important…linguistics and voice search
  21. [explain]
  22. [read]
  23. We think different things than we type. Let’s say we want to buy a new phone. We often think like ‘subjective’ but we don’t write/type it.
  24. Semantic engines should factor this in. Today I present a newer version of ‘informational, navigational, transactional and connectivity’ They are direction, explicitly typing what we think Transformed – some use of thinking None, staying with old-school search patterns Voice search uses significantly less nouns compared to typed KWs
  25. First thing’s first, the term mobile is misleading. Most searches occur when we are at home or at work, we’re general static when we carry out a search
  26. Mobile are utilities, much like: gas, water and electric. This is why Google said that 91% of people have their mobile in a 3 meter radius 100% of the time Although we are starting to understand the human factors of mobile, mobile, as an area of research, is still in its infancy
  27. Boredom is not someone who has nothing to do, it is someone who is actively looking for stimulation but unable to find something stimulating
  28. http://metro.co.uk/2015/04/28/how-many-trees-would-it-take-to-print-every-single-page-of-the-internet-5171497/ http://www.physics.le.ac.uk/jist/index.php/JIST/article/view/100/57 This year the web is only 26 years of age In 2014, 40% of people in the world are using the web 68 billion