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

1. The real reason why Google Hummingbird exists Friday, 22nd April 2016
2. Introduction Gerald Murphy
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
11. Types of semantic search engines
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
23. Keywords
24. Keywords Environment Gender Age Location Culture Linguistics Voice search Use of words and phrases
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
29. Mobile
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
42. Thank you!

Hinweis der Redaktion

Hello, thanks for staying. I’m dying for another drink too…
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
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
We’ll do this by weaving in solid understanding from the 8 usability principles
V. Bush
Automated bots
Web inception
Engines to organize mess
Loads of definitions of web search…
But, one thing we can agree on is…
…[read]
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
Babysitting anology
There are 4 types, let’s take a look
The first is SHOE
Think forms and databases
And think Schema
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
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
Both SHOE and RDF rely on additional code, basically Schema, it is no wonder why all the major engines have agreed on Schema code
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
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.
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.
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
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
7 factors influence our choice of KWs

For semantics, 2 are particularly important…linguistics and voice search
[explain]
[read]
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.
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
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
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
Boredom is not someone who has nothing to do, it is someone who is actively looking for stimulation but unable to find something stimulating
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

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