Dawn takes a look at ‘The Iceberg Approach to SEO’. As we move increasingly to an era of smaller screen search (or no screen), we need to consider ways to say more with less and communicate this to both search engines and users. She explores semantics, the knowledge graph, schema and ontologies combined with UX as methods to pass themed ‘equivalence’ from below the surface of the site or the individual page.

1. ”Power from what
lies beneath... The
Iceberg Approach
to SEO”
“Task-driven search for the
time and attention poor -
overwhelmed, mobile-first
surfer“
Dawn Anderson
@DawnieAndo from
@MoveItMarketing

2. WHO
IS
DAWN
ANDERSON
@dawnieando
dawn.anderson@@move-it-
marketing.co.uk
move-it-marketing.co.uk
linkedin.com/in/msdawnanderson
11+ years SEO & Digital Marketing Consultant
& Pracademic:
Contributor:
Speaker & Trainer:
Fellow of:

4. A perfect storm is building

5. Evolution of
Information
Retrieval
Classic
Information
Retrieval (IR)
Interactive
Information
Retrieval (IIR)
Mobile
Information
Retrieval (MIR)
Machine
Learning

6. Mizzaro’s Framework of
Relevance
• Information resources
• User problem
• Time
• Components

7. Evolution of
Information
Retrieval
Classic Information Retrieval (IR)
Traditional 10 blue links

8. Evolution of
Information
Retrieval
Interactive Information Retrieval (IIR)
Machine
Learning
Human is in the loop refining with feedback until the informational
need is met appropriately

9. Evolution of
Information
Retrieval
Mobile Interactive
Information Retrieval (MIIR)
Machine
Learning

10. Time dimension of
framework of
relevance extended
by Cappala to time-
space dimension in
mobile information
retrieval…
Because the user is
on the move

11. Accelerometer
And… the speed at which they
are moving impacts relevance

12. "Mobile IR is
growing much
faster than IR"
(Crestani et al,
2017)

14. Something’s brewing…‘A Call to Arms’
in ‘Assistive AI’

15. … and some names
Subordinate systems
Conducive systems
Decisive systems

16. These are the names of the ‘Assistive systems
categories’ given by Broder, 2018

17. When Andrei Broder rocks
up and starts giving things
names…

18. It’s time to
wake up…
and smell
the coffee

19. You might know some things
he named already in 2002
ATaxonomyofWebSearch
Informational
Transactional
Navigational
Broder, A., 2002, September. A taxonomy of web search. In ACM
Sigir forum (Vol. 36, No. 2, pp. 3-10). ACM.

20. “Formally, the
assistive systems can
be viewed as a
selection process
within a base set of
alternatives driven by
some user input.”
(Broder, 2018)

21. Layman’s terms:
10 blue links & a search
box is just now one of
very many ways to be
assisted via search
systems

22. Ubiquitous,
pervasive
computing

23. So… back to exploring
these systems

24. Conducive Systems
(Broder, 2018)
Ten blue links &
a search box

25. DECISIVE SYSTEMS
(Broder, 2018)
• All decisions are automatically made
• Ambiguities are resolved
• No further input (refinement) is
needed by the user
• EXAMPLE: Translation systems, self-
driving cars

26. SUBORDINATE SYSTEMS

27. • Push IR• Pull IR
• Pull
Marketing
• Push
Marketing
Sales / outbound
advertising
Draw customers
via SEO / Content
/ inbound
Recommending
/ assistive
proactive query
suggestions /
task-helping
Waiting for
Search Engine
User to enter a
query (re-active)

28. Enter > Recommender Systems

29. What does the modern assistive search system
user look like?

30. They are time
poor and
research crazy

31. Mostly searching with
one eyeball and one hand

32. Multi-devicing (even hugely so at home)

33. Their attention
span is not that
short, but we are
in an ‘Attention
Economy’
I can concentrate for longer
than you… NO YOU CAN’T
MYTH

34. Information
overload
madness

35. Remember the
‘Jam Study’?

36. THE
ATTENTION
ECONOMY
Information
overload
Too much
content
We are all
researchers
Agile task-
driven
searchers
‘Noisy’
information
systems

37. SEO
IN A
CORNER
NOBODY PUTS

38. You noticed mobile phone
screens were small… right?

39. We need to think about icebergs & iceberg approaches in modern
mobile-first and assistive SEO

40. 57%
We need to think about
…Iceberg Syndrome

41. Iceberg Syndrome &
‘Reasonable Surfer’ combined

42. And …Iceberg
Theory

43. Less is sometimes so much more -
Do the Chunky Monkey, Do the Swan, Do the
Bear

44. Thinking about information overload and using mental heuristics to ‘chunk’
Do the
chunky
monkey

45. Clustering and
chunking

46. INFORMATIONAL
CLUSTERS &
WORKING
MEMORY
‘CHUNKING’
https://www.nngroup.com/articles/chunking/

47. Concise
brevity &
directional
heuristics
TL:DR
Exec
Summary
AbstractShort Answer
Table of
Contents

48. Page Jumps
<center>
<a href="#Top">Top</a>
&nbsp;|&nbsp;
<a href="#Bot">Bottom</a>
</center>
<center>
<a href="#Top">Top</a>
&nbsp;|&nbsp;
<a href="#Bot">Bottom</a>
</center>
<a name="Top"><a name="Bot">

49. Think Gestalt Principles & Adaptive Web Design

50. Our minds are thought to understand patterns and fill in gaps

51. We will look
at 3 main
Gestalt
principles
Proximity
Similarity
Closure

52. Gestalt
Principle -
Proximity

53. Gestalt Principle - Similarity

54. Gestalt Principle
– Closure -
Reification
– What do you
see?

55. The ‘Long Form’ Content Myth

56. Because… it
depends
Task dependent
‘BM25 Fails’
Theory
Page Length
Normalization -
suppression
Probably ignored
on transactional
pages
Niche dependent
Singhal, A., Buckley, C. and Mitra, M., 2017, August. Pivoted document
length normalization. In ACM SIGIR Forum (Vol. 51, No. 2, pp. 176-184).
ACM.

57. “The Brain processes
visuals 60,000 times
faster than text”
– 3M Corporation
Research

58. Their attention
span is not
that short, but
we are in an
‘Attention
Economy’
I can concentrate for longer
than you… NO YOU CAN’T
MYTH

59. Be interactive… be
adaptive… think Gestalt

60. Because… Google Is

61. Calm on the surface… Furiously semantically powered from beneath
Do the
swan

62. Power yo
unstructured data
with collection co-
occurring semi-
structured,
relational
databases &
structured data
markup

63. Unstructured
data (text)
Semi-
structured
data
Relational
databases
Structured
data
XML sitemaps Ordered lists
Unordered
lists
Tabular data Data Feeds

64. Database wins every time

65. Build a ontological lexicon

66. Jump Pages

67. Navigation is King for humans & search engines

68. Utilise the power of rich relevant text combined
with structure from navigational systems

69. Structured & semi-structured task-driven and contextual internal links

70. Breadcrumbs
• Global
• Sectional

71. Types of Internal links
• Audience defined
• Chronological
• Alphabetical
• Step navigation
• Task driven navigation
• Conceptual navigation
• Hierarchical categories
& subcategories
• Breadcrumbs
EVERY SINGLE MENU ON
YOUR SITE IS AN
OPPORTUNITY TO ADD
STRUCTURE & POWER

72. Also a pretty good
antedote for ‘crawl
budget’ issues…
There… I said the words
”crawl budget”… aargh

73. Identify as many navigational
assistive routes to the most
important pages as possible
Too many near duplicate
pages?
Antedote: -> Switch

74. IsSimilarTo Schema

75. YOU NEED A
SITEMAP
STRATEGY
Image credit: Wikipedia
•Visual sitemap
•XML sitemap
•HTML sitemap

76. XML
Sitemap(s)
Visual
Sitemap(s)
All parties
informed &
educated on
importance
Front facing
HTML
sitemap(s)
Trio of
sitemaps BOTS
HUMAN
VISITORS
SEO /
DEVS /
CONTENT
TEAM

77. Take a birds eye view of your document collection

78. Internal link
visualisations

79. GREAT TOOL:
Convertcsv.com
Image credit: Wikipedia
• URL EXTRACTOR TOOL
• EXTRACTS TO EXCEL FOR EASY
MANAGEMENT

80. Use 1st & 2nd Level Relatedness

81. Machine Learning
Word2Vec & Concept2Vec

83. Relatedness for Disambiguation –
Co-occurence Vector Windows
First Level
Relatedness
Co-
Occurrence
Vectors &
vector
window
Second
Level
Relatedness

84. Watch out for scissor happy ‘pruning’

85. IsRelatedTo Schema

86. Use structured data… of course

87. The Web of Data versus The Web of Document

88. “The Web as I envisaged
it, we have not seen it
yet.” (Sir Tim Berners-Lee)
(2009)
Inventor of The World
Wide Web & ‘Linked Data’

89. Structured
data explosion
2017
2014

90. SEOs have definitely helped to start
to turn the web into a web of data

91. 25,471
88,467
56,307
148,960
2017 2018
STRUCTURED DATA IMPLEMENTATION YoY
2018 versus 2017 – 3 x Growth
JSON-LD
MicroData

92. • Information resources
• Can be a descriptor (not necessary to
have a document) – a ‘thing’ may well
suffice
• Sometimes there is no need for a
document at all
• Also read the work of Cindy Krum on this
point
Cappala extends
Mizzano’s ‘Framework
of Relevance’ for
Interactive Mobile

93. So…Schema
‘All The Things’
X 3
implementation
YoY

94. Growth of near me ‘now’

95. AreaServed Schema likely useful here

96. Test your structured data

97. Work on ‘Google My
Business’
Maximise GMB

98. Sequentially foraging for information to complete tasks and sub-tasks
Do the
bear

99. Sequential task-driven predictive search (not
overly user-modelled or overly personalised)

100. Because… for example

101. It’s not very much about
you any more… but more
the tasks you and others
like you do

102. BUT… HUMANS ARE INFORMAVORES
We need, and thrive off, information

103. Predictive task-driven search is like an RPG…
’
Tasks are
pretty
predictable…
as too are
their subtasks
Nogueira, R. and Cho, K., 2017. Task-oriented query reformulation with reinforcement learning. arXiv preprint
arXiv:1704.04572.

104. TASK PREDICTION & TIMELINES & SUBTASK IDENTIFICATION
MOT
anyone?
De Oliveira, R. and Pentoney, C., Google LLC, 2018. Methods, systems, and media for presenting a user interface
customized for a predicted user activity. U.S. Patent Application 15/234,446.

105. Tasks !=
sessions

106. Potentially
huge search
engine
efficiencies…
prefetch
document?

107. Do the bear… Using hints, scents &
patches – Optimal foraging theory
(Pirolli & card)

108. Understand your bears…Ask customers, staff & panels

109. TOP-TASK IDENTIFICATION & SEQUENCE
Gizza Job
Gizza Job

110. Sequence
signposting…
Where to
next?

111. To summarise…

112. A search UI being agilely
redesigned over time
with adaptive content
and responsive user
design and Gestalt
principles.
UI for the overwhelmed
mobile-first user in an
attention economy.

113. Attention Economy & Information Overload – Chunking
& task assistive recommending (Push IR)
Gestalt Principles Mobile Search UI & Sunburst type
radial data visualisation with Adaptive web design
Berry picking - Human information foraging theory –
information scents & information patches
Relatedness - Conceptual 1st & 2nd level context &
concept disambiguation
Immediacy / Space + Time dimension (Geo location
+ speed of movement)

114. Thank you
Keep in touch
@DawnieAndo
@MoveItMarketing

115. References
• https://www.seroundtable.com/google-search-tab-bar-navigation-25824.html
• Nielsen Norman Group. 2018. How Chunking Helps Content Processing. [ONLINE]
Available at: https://www.nngroup.com/articles/chunking/. [Accessed 02 June 2018].
• Nogueira, R. and Cho, K., 2017. Task-oriented query reformulation with reinforcement
learning. arXiv preprint arXiv:1704.04572.
• Dumais, S., 2013, March. Task-based search: a search engine perspective. In NSF
Workshop on Task-Based Search (Vol. 1).
• Park, D., Kim, S., Lee, J., Choo, J., Diakopoulos, N. and Elmqvist, N., 2018. ConceptVector:
text visual analytics via interactive lexicon building using word embedding. IEEE
transactions on visualization and computer graphics, 24(1), pp.361-370.
• Image attribution - By JasonHise at English Wikipedia - Transferred from en.wikipedia to
Commons., Public Domain,
https://commons.wikimedia.org/w/index.php?curid=1724044

116. References
• De Oliveira, R. and Pentoney, C., Google LLC, 2018. Methods, systems,
and media for presenting a user interface customized for a predicted
user activity. U.S. Patent Application 15/234,446.
• Medium. 2018. Hamburger menu alternatives for mobile navigation –
Zoltan Kollin – Medium. [ONLINE] Available
at: https://medium.com/@kollinz/hamburger-menu-alternatives-for-
mobile-navigation-a3a3beb555b8. [Accessed 02 June 2018].
• Verschure, P.F., Pennartz, C.M. and Pezzulo, G., 2014. The why, what,
where, when and how of goal-directed choice: neuronal and
computational principles. Phil. Trans. R. Soc. B, 369(1655),
p.20130483.

117. References
• Maxwell, D. and Azzopardi, L., 2018, March. Information Scent,
Searching and Stopping. In European Conference on Information
Retrieval (pp. 210-222). Springer, Cham.
• Hua, W., Song, Y., Wang, H. and Zhou, X., 2013, February. Identifying
users' topical tasks in web search. In Proceedings of the sixth ACM
international conference on Web search and data mining (pp. 93-
102). ACM.
• Richardson, M., 2008. Learning about the world through long-term
query logs. ACM Transactions on the Web (TWEB), 2(4), p.21.

118. References
• Singhal, A., Buckley, C. and Mitra, M., 2017, August. Pivoted
document length normalization. In ACM SIGIR Forum (Vol. 51, No. 2,
pp. 176-184). ACM.
• Singhal, A., 2001. Modern information retrieval: A brief overview. IEEE
Data Eng. Bull., 24(4), pp.35-43.
• Ong, K., Järvelin, K., Sanderson, M. and Scholer, F., 2017, August.
Using information scent to understand mobile and desktop web
search behavior. In Proceedings of the 40th International ACM SIGIR
Conference on Research and Development in Information
Retrieval (pp. 295-304). ACM.

119. References
• Broder, A., 2002, September. A taxonomy of web search. In ACM Sigir
forum (Vol. 36, No. 2, pp. 3-10). ACM.
• Broder, A., 2018, February. A Call to Arms: Embrace Assistive AI
Systems!. In Proceedings of the Eleventh ACM International
Conference on Web Search and Data Mining (pp. 1-1). ACM.
• Hua, W., Song, Y., Wang, H. and Zhou, X., 2013, February. Identifying
users' topical tasks in web search. In Proceedings of the sixth ACM
international conference on Web search and data mining (pp. 93-
102). ACM.
• Nogueira, R. and Cho, K., 2017. Task-oriented query reformulation
with reinforcement learning. arXiv preprint arXiv:1704.04572.

120. Wang
Mizzaro Scagnetto
Church Chen
Crestani
Vassena
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