Marketing AI - How to Build a Keyword Ontology

© 2015 IBM Corporation
Marketing AI:
How to build a keyword ontology
James Mathewson
Michael Priestley
Dan Segal
MinneWebCon 2018

Presentation agenda
Introductions and bios
What is a keyword ontology?
Why is it important?
Some basics of ontology
How do you build one?
Use cases
• Think Case study
• Journey mapping
• Autotagging
Exercises

James Mathewson
James Mathewson is
IBM's Distinguished
Technical Marketer for
search. He has 20 years of
experience in web
editorial, content strategy,
and SEO for large and
small companies. A
frequent speaker, lecturer
and blogger, James has
published more than 1600
articles and two books on
how web technology and
user experience change
the nature of effective
content. James has two
advanced degrees on
related subjects from the
University of Minnesota.

Michael Priestley
@ditaguy
Michael Priestley is a product owner and
content technology strategist, currently leading
the IBM Marketing Taxonomy Guild to revise
and align taxonomy initiatives across the
marketing ecosystem. He has experience
working with and across documentation,
support, training, and marketing content as an
enterprise content technology strategist. He
was one of the original architects and editors of
the DITA standard, was named an OASIS
Distinguished Contributor in 2017, and is
currently co-chairing the Lightweight DITA
subcommittee.

Dan Segal
Dan Segal is a Taxonomist on IBM's Marketing
Platforms team. His work focuses on the use
of cognitive technologies, including natural
language processing and machine learning, for
SEO and digital marketing. Dan has over 15
years of experience as a practitioner and
consultant in the areas of taxonomy and
ontology development, vocabulary
management, semantic metadata, text
analytics, and information retrieval. He holds
BS and MLS degrees from Rutgers University.

What is a keyword ontology?
A keyword ontology is a knowledge graph describing relationships
between the keywords your target audiences frequently use in search
queries and the content about the products and services you sell

Keyword Data
Topics
Personas
Job Roles
Industries
Products Solutions
Events
Buyer
Journeys
Narratives
BUs Brands

Non-branded
keyword group
Non-branded
keyword group
Branded
keyword group
Business UnitProduct SegmentKeyword Groups
(Taxonomy)
Keywords

Ontology: Formal Representation of Knowledge
Resources: What is this thing?
Properties: What characteristics does it have?
Relationships: What connections does it have to other resources?
Constraints: What rules apply to this resource?

Here is a Thing. Now Let’s Describe It.
Property Value
Name Diet Coke®
Manufacturer Coca-Cola
Container type Plastic bottle
Container size 20 oz.
Color Brown
Flavor Saccharine Cola
Sugar? No

Ontology: Representing Knowledge through Graphs
makes made by
Thing Y
also known
as
name Diet Coke®
size
20 oz.
calorie
s
0
name
Coca-Cola Cherry®
Cherry Coke®
The Coca-Cola Company
name
Thing X
makes
made by
Thing Z

Ontology: Semantic Triples
Declarations are expressed as <subject , predicate, object>
– Subject is a resource
– Predicate is a property
– Object can be another resource, or a data value
Subject Predicate Object
Resource Property
Resource
Data value
or
Adapted from McComb and Robe: Designing and Building Business Ontologies

Semantic Triples: Examples
name
Diet
Coke®
Thing X
Thing X hasName Diet Coke®
made by
Thing X Thing Y
Thing X isMadeBy Thing Y
name
The Coca-Cola
Company
Thing Y
Thing Y hasName The Coca-Cola Company
Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl
15

Expressing Triples in Markup Language
<rdf:Description rdf:about="#ThingX">
<rdf:type rdf:resource="#Products"/>
<rdfs:label xml:lang="en-us">Diet Coke</rdfs:label>
<madeBy rdf:resource="#ThingY"/>
<calories
rdf:datatype="http://www.w3.org/2001/XMLSchema#integer">0</calories>
<size rdf:datatype="http://www.w3.org/2001/XMLSchema#string">20
oz.</size>
</rdf:Description>
Here is our graph for “Thing X” (Diet Coke®), expressed in
computer-readable language:
16

Google is the best source of information on your target audience

3,000,000,000,000 queries in 2016

Why do we need a keyword ontology?
Better content
• Make better
content decisions
• Learn from others
• Measure
categories and
combinations
Less waste and
confusion
• Know what exists
• Reuse or
differentiate
Drive experience
• Navigation
• Progression
• Personalization

Agile development
for better content, less waste and confusion
Do we
have
it?
Did it
work?
What
do they
want?
Plan
Measure
Manage
Publish

Requirements for agile development
People
ProcessTools
Cultural shift from
organization
centric to customer
centric
Governance across
tools and authoring
communities
Cognitive tagging for quality
assurance and content coverage

Driving experience
Navigation
• What’s similar
• Cognitive linking
Progression
• What's next
• Ontologies
• Cognitive CTAs
Personalization
• What's for me
• Predictive linking
• Cognitive
experience

Cognitive delivery
to drive customer experience
Do we
have
it?
Did it
work?
What
do they
want?
Customer awareness
Improvement in selection
Content awareness
Measures of success

Requirements for cognitive delivery
Common
customer data
Common
content
metadata
and
ontologies
Common
linking
components
Plus common KPIs, common process for improvement...

More than just topics/keywords
•What is the purpose of the content? Does it explain a concept, express an opinion, describe a case study?
•Example types: Explainer, Opinion/POV, Case studyContent type
•How is the content expressed? Is it a web page? A PDF? A video animation?
•Example formats: Web article, Downloadable PDF, Video animationContent format
•Where did the content come from?
•Example publisher types: Client, Partner, AnalystPublisher type
•What is the content about?
•Example topics: blockchain, data science, cloud computingTopic
•Who is the content for?
•Example audiences: data scientist, CFO, doctorAudience/role
•What industries is this content relevant in?
•Example industries: aerospace, energyIndustry
•What IBM offerings or offering categories does the content describe?
•Example products/solutions: Watson HealthcareProduct/Solution
•When is the content relevant to the intended audience?
•Example stages: Learn, Try, BuyBuyer stage

The challenge of enterprise content strategy
• 200K relevant English keywords
• 10 other languages with at least 100K relevant keywords
• 300 million pages
• 100K assets: Videos, white papers, case studies, demos, etc.
• Total opportunity = 300 million users
• More than 100 personas
• Uncertainty
• How do we know that the 1.2 M keywords is even the right set?
• Assuming it is, how do we prioritize opportunities?
• How do we determine who should own the page/asset that is the best
answer for the question implicit in the query?

29
How to build a keyword ontology

30
Audience intent is what people need to do with the
information they seek

Informational
Navigational
Transactional
Three categories of audience intent
I don’t know the topic, but I want to start learning about it. Where do I start?
I know the topic, but what’s the best page for reference on it?
What’s the best place to buy this thing, or get help for that thing?

When users search with a
“what is” query, they are just
starting to learn about the topic
EXAMPLE
The best way to learn the
audience intent for a query is
by reading the search results,
and gathering clues about the
audience and infer what they
are trying to do

Three Steps to Building a Keyword Ontology
Identify keywords
Determine semantic clusters (Concepts)
Associate Concepts to each other

Tools for Building Your Keyword Ontology
Tool Use Examples
Keyword research • Identify high-value keywords
• Identify high-ranking URLs
• BrightEdge
• Google Keyword Planner
• Ubersuggest
Natural language processing (NLP) • Keyword / entity extraction • GATE
• OpenNLP
• Watson NLU
Machine learning • Text classification
• Keyword clustering
• scikit-learn
• Watson NLC
Data transformation • Convert ML outputs (e.g. JSON)
to RDF
• OpenRefine
Ontology management • Domain modeling
• Keyword / Group associations
• PoolParty
• Protégé
• TopBraid EVN

Cognitive Keyword Classifier
§ Select seed keywords from KIS
§ Identify top-ranking pages (IBM and non-IBM)
Select portfolio-segment
Keywords
Use AlchemyAPI to scrape
SERPs and identify
recurring Concepts
Normalize Concepts to
taxonomy
Train Watson NLC on
Keyword/Concept pairs
Classify Keywords using
Watson NLC API
Refine training data to
improve performance
§ Scrape text of ranking pages (60-70 pages / minute)
§ Apply NLP and machine learning algorithm
§ Identify clusters of co-occurring Keywords and
Concepts
§ Consolidate Concepts
§ Harmonize to existing IBM vocabularies
§ Train Classifier on seed data
§ Measure Recall and Precision using test data
§ Submit keywords to Classifier
§ Process JSON output
§ Flag incorrect or low-scoring test classifications
§ Re-train the Classifier as needed

Training Data: Keyword / Concept pairs
“Data scientists spend 60% of their
time on cleaning and organizing data.
Collecting data sets comes second at
19% of their time, meaning data
scientists spend around 80% of their
time on preparing and managing data
for analysis.”
- Gil Press, Forbes magazine

Keyword Classification: Sample Workflow
INPUT: keyword list (CSV)
Bluemix API
OUTPUT: Watson NLC classification (JSON) POST-PROCESSING: Watson NLC classification
(transformed to CSV)

Classifier Output: Weeding Out
Weak Matches and “Laughers”
§ Limit classifier confidence score to exclude:
– Weak classification matches
– Noisy or marginally-relevant keywords
§ Low scores can also indicate candidate Concepts
§ Test threshold score: ≥ 0.25 for “keepers”
§ Ask: Do the training data introduce bias?

Connecting
the parts
Keyword data
(from autoclassifier)
Keyword Group
(from Topic taxonomy)
Product Segment
(from Product taxonomy)

Uses: Microsite design
41

Example flow – from first click to consult
Content page
Recommended
page
Pages on
same topic
CTA to event
Register for
event
Personalized
recommended
pages
Subscribe to
newsletter and
notifications
CTA to
consultation

Uses: Keyword governance
44

Uses: Keyword Tracking and Measurement
Keyword Groups
(defined in ontology;
exported to SEO platform)
SEO Platform
(keyword reporting)
45

Uses: Journey mapping
46

Two kinds of audiences: business people and specialists
Business people tend to use
informational queries to learn about
topics and products that are not in their
core areas of expertise
They also use transactional queries to
move towards purchase
Specialists use a lot more
navigational queries to get reference
information about topics or products
within their areas of expertise.
They also use social networks to
connect with influencers on these topics 47

Emily
CMO
Cynthia
CEO
Jan
Digital
strategist
Loc
Data scientist
Marco
Finance lead
Jeff
Procurement
lead
Sarah
Marketing
manager
Specialist Business Person

Two kinds of queries: unbranded and branded
Audiences start with unbranded queries, and only move to branded queries when they’re ready
What is big data?
Why is AI so important for
big data?
How does IBM solve big data
problems with AI?
How can I solve my big
data problems with IBM?
What is Watson Analytics?
How is Watson Analytics better
than the competition?
How do I try
Watson Analytics?
How do I buy
Watson Analytics?
49

Discover Learn Solve Try Buy
What is
big data?
Big data for
marketing
Big data
platforms
Watson
Analytics
free trial
Watson
Analytics
ROI
Emily CMO

Uses: Autotagging
51

Using Keywords to Drive Auto-tagging
• Starting point: Topic taxonomy
• Derived from content analysis and keyword research,
as well as competitive benchmarking
• Extended taxonomy with custom properties:
• Text signals that are used to train the auto-classifier
• Phrases derived from iterative analysis

Cognitive Tagging Service: Sample Output
IBM Case Study
Extracted text signals
Call to NLU
with custom
annotation
model via
REST API
Query via SPARQL endpoint to match
extracted signals to controlled values
using skos:prefLabel,
skos:altLabel, etc.
Metadata values
(extracted text signals, normalized to standard
vocabulary and relevance-ranked)Keyword-enriched taxonomy

Continuous Improvement and Validation
Content
creation
Keyword
research
Unmatched entities
Entity
extraction /
normalization
Results
logging and
review
Taxonomy
enrichment
Model
optimization
Document
processing

Group Exercises
55

Scenario:
• You are an entrepreneur with a vision to sell your specialized line of ice cream to health-conscious
consumers.
• The market is highly competitive, and you need your content to be findable.
• As part of your digital marketing strategy, you need to map the knowledge domain of healthy frozen
desserts so that you can identify high-value keywords and incorporate them into your content.

Exercise 1: Keyword Identification
• Analyze Web content from your subject domain.
• Examine a variety of sources: references, news, magazines, trade publications, blogs, reviews, etc.
• Identify keywords that represent your domain.
• Write each keyword on an index card.
Resources: sites.google.com/view/keyword-ontology
Seed keywords:
• healthy ice cream
• organic ice cream
• low-fat ice cream
• low-calorie ice cream
• vegan ice cream
Keyword research tools:
• k-meta.com
• serpstat.com
• neilpatel.com/ubersuggest

Exercise 1: Keyword Identification: ”Quick and Easy”
Example: k-meta.com
• Search your seed keyword; e.g., healthy ice cream
• On the Keyword Overview page, scroll down to Organic results
• Click on top-ranking URL
• Select high-volume keywords (record on cards)
• Repeat for additional URLs and keywords
Resources: sites.google.com/view/keyword-ontology
Seed keywords:
• healthy ice cream
• organic ice cream
• low-fat ice cream
• low-calorie ice cream
• vegan ice cream
Keyword research tools:
• k-meta.com
• serpstat.com
• neilpatel.com/ubersuggest

Exercise 2: Card Sorting
• Review the keywords that you identified in Exercise 1.
• Arrange the cards into groups that make sense.
• Name each group.
• Present your findings and lessons learned.
Tip: Group together keywords that are:
• Synonyms (ex.: cars, automobiles)
• Stemmed forms (ex.: project management, project
managers)
• Conceptually related (ex.: apples, peaches, pears,
cherries, etc.)
Tip: You can arrange groups into a hierarchy.
• Dogs
• Hounds
• Sporting breeds
• Toy breeds
• Terriers
Remember: There is no single correct answer. The objective is to define organizing principles and
then group the keywords accordingly.

Marketing AI - How to Build a Keyword Ontology

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Marketing AI - How to Build a Keyword Ontology

Similar to Marketing AI - How to Build a Keyword Ontology (20)

Recently uploaded

Recently uploaded (20)

Marketing AI - How to Build a Keyword Ontology