Semantic technology solves a concrete problem: sorting through large collections of unstructured data. With unstructured data expected to account for over 80 percent of all data within the next five years, the ability to target this data using semantic technology opens doors to better understand context and intent in search, and ultimately achieve greater visibility and relevant results. This presentation will showcase how semantic technologies can understand article content and select contextually relevant adverts to most effectively monetize content for advertisers and publishers.
* Discover how semantic technology helps thousands of advertisers reach their audience in a meaningful way, and helps hundreds of online publishers monetize their content.
* Learn how semantic technology reduces errors and time spent by marketers when defining their campaigns and yield better overall results.
* Understand some of the challenges that come with semantic technologies and how to address them ahead of time.
* Go behind the scenes with semantic and linguistic processing of web pages.
3. Overview
• Campaign management challenges related to
paid search advertising
• Semantic technologies to the rescue
• Managing a large semantic indexing engine
• Semantic technologies in action
Time for questions at the end….
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5. Paid Search Advertising
Agency
Ad
Network
Consumer
Publisher
Product or
Service
5
Advertiser
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6. Building a Campaign
What you do:
• Create new campaign
• Write your ads
• Landing pages
• Conversion tracking
• Fund campaign
What? Wait! Why?
• Set up and optimize
keyword campaigns
• Become an expert at
keyword selection
– negative keywords, quality
score, broad matching,
long tail, bid management,
search query report
• Acquire tools to help in
the process
• Rinse, Repeat …
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7. Common Problems with Keyword Selection
• Target High Value or Long Tail
• Deal with Ambiguity
• Filter Unwanted Articles (and Keywords)
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8. Selecting Keywords
• Target affordable popular
queries?
• Target expensive specific
queries?
• Target large volume of
cheaper long tail queries?
• Am I forgetting
any keywords?
Keyword Competition
Global
Monthly
Searches
an attorney Medium 13,600,000
attorney in Medium 13,600,000
lawyers in High 11,100,000
attorneys Medium 7,480,000
attorneys in Medium 7,480,000
lawyers attorneys High 6,120,000
attorney lawyer High 6,120,000
… … …
attorney accident High 368,000
attorney for accident High 368,000
lawyers accident High 368,000
lawyers for accident High 368,000
Source: Google Adwords
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9. Addressing Ambiguity
How do I handle ambiguous keywords?
Should I show an ad for Which apple did you mean?
Windows or windows?
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10. Filtering Unwanted Articles (Negative Keywords)
Both Articles Contain the Words “Camera” and “Killing”.
Should you include “Killing” as a negative keyword?
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11. It Is Not Simple!
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12. In Summary
Traditional campaigns management is not trivial:
• Select the right keywords
and the right price
• Avoid undesirable content
• Continuous tuning
• Spend time and money on a task
a machine can do
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13. What is the Alternative?
Use semantic technologies:
• Select all possible concepts
(and associated words)
relevant to a product
• Provide disambiguation
(distinguish between
meanings)
• Match relevant adverts to
articles with concepts rather
than keywords
• Flag negative themes and
undesirable content
Benefit for advertisers:
• No need for keyword
management and difficult
keyword targeting decisions
• No steep learning curve to
advertise online
• More time to concentrate on
creative aspects of your
campaign
• Protect advertisers brand
Benefit for publisher:
• More targeted ads, in line with
article content yields more
revenue
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14. A Primer On
SEMANTIC AD MATCHING
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15. The Concept
How would a human do it?
1. Look at an article, its theme
2. Look at all available adverts,
what they are about
3. Select adverts that have most
concepts in common with the
article
4. Pick the most relevant adverts
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16. Semantic Indexing of Article and Adverts
Publisher Article
(BHG.com)
Semantic Ontology
Product Advert
& Landing
Page
Product Advert
& Landing
Page
Engine
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17. Advert Semantic Indexing
Advert and Landing Page (TYRRELL & LAING INTL)
Concept Rank
bathtub (shower or tub) 2.0000
Stone (Masonry) 1.1340
shower or tub (bathroom product) 0.5000
accommodation/lodging (travel service) 0.4433
plumbing supply (building supply) 0.3299
plumbing product (products) 0.3299
condo (home type) 0.2887
range (cooking appliance) 0.2680
jacuzzi tub (pool and pool maintenance) 0.2474
houseware (products) 0.2062
bathroom product (products) 0.1186
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24. Flag Negative Themes and Undesirable Content
Concept Rank
dictator (authoritarian) 104
Serbia (Geographical Region) 74
Balkan State (Geographical Region) 57
Beograd (city) 54
The Observer (UK newspaper) 46
garden (cultivated land) 38
former head of state (statesmen) 34
ex-president (former head of state) 31
the Hague (city) 26
coffin (burial container) 25
Yugoslav (Historic Nation) 20
funeral ceremony (professional service) 20
husband (mate) 20
presidents family (Family) 18
town (municipality) 18
married woman (mate) 17
son (male offspring) 17
socialist party (political party) 17
man (male person) 15
hero (person) 14
minority (Ethnicity) 14
lime tree (Citrus amara) 13
prison cell (cell) 13
war criminal (criminal) 13
Id Facet Name
death_Topic Death
unfortunateEvent_Topic Unfortunate Event s
war_Topic War
Fertilizer company
may want to skip advertising
on this page
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25. Semantics Brings Relevance
Let the machine do the tricky part!
• Operates at the concept
level, not keywords
• Handles disambiguation
• Facet classification
is more subtle than
negative keywords
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26. Relevance Improves Monetization
• No need for keyword
management
• Integrated brand safety
• No spending on
additional tools
Do What You Do Best:
• Build new campaign
• Write your ads
• Optimize landing pages
• Track conversion
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27. What It Takes To Engineer the
SEMANTIC INDEXING ENGINE
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28. Two Main Components
Semantic Indexer Ontology
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29. Large Ontology
• 500K Concepts
• Over 2.5M Expressions
(language-specific)
• Links between concepts
• Dozens of domains
• Broad coverage allows
ontology to capture ambiguity
• Rule-based facets track
offensive language,
adult content, unfortunate
events
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Intermediate
Specific
Lepidoptera
Butterfly
Generic
Living Being
30. Semantic Indexing
• HTML Parsing
• Language Detection
• Tokenization and stemming
• Boilerplate Detection
• Concepts identification and
disambiguation
• Entity extraction
• Rule-based & Bayesian
faceting
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31. Example: King Disambiguation
I defeated the king
with a rook.
I won the king with
an ace.
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32. 32
King Specialization in the Ontology
king king
More Generic
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33. 33
Activation and Disambiguation
I defeated the king with a rook.
Screenshot of NTENT’s visualization tool Confidential – Property of NTENT™
34. 34
Activation and Disambiguation
I won the king with an ace.
Screenshot of NTENT’s visualization tool Confidential – Property of NTENT™
35. Maintaining a Large Ontology
• Ontology is source
controlled
• Tools to edit, visualize
ontology
• Consistency checks to
validate structure
• Daily TREC-like test to
control quality
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43. Daily Quality Checks
Screenshot of TREC-style quality evaluation 43
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44. Robust Semantic Platform
• Large ontology with deep coverage in
dozens of verticals
• Continuous improvement backed by
powerful visualization and editing tools
• Daily consistency and quality assessment
• Improvements benefit all
advertisers and publishers
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46. Designed for publishers looking to monetize their content more effectively, our NTENTLink™ widget provides
a better ad experience by matching relevant ads to publisher content based on page-level relevance.
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CONTENT MONETIZATION: NTENT ADS
Powered with “intelligence” that understands the context of every web page, NTENTLink™ targets ads with
unparalleled relevance.
47. Personalize the way users discover video content with our Video Recommendation Engine. It analyzes users’
interests and site content to fuel intelligent and relevant video recommendations in real-time.
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VIDEO RECOMMENDATIONS
1. Semantic Analysis:
Our semantic engine
conducts page-level content
analysis
2. Personalization:
We surface relevant video
recommendations based on
user interest
3. Engagement and Monetization:
Users click to play, an overlay expands
and a video pre-roll is served prior to
the main video content
48. Give your users access to a faster, more relevant way to navigate the web directly from your site. NTENT’s
cross-platform search application integrates easily into your site, inside your apps or on your mobile site.
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CONTENT MONETIZATION: NTENT SEARCH
Site or Browser Install In-App Mobile Browsers
Every search query generates a new revenue opportunity for you, giving you access to additional ad
impressions and the ability to increase time spent on your site.