Lezing over het opzetten van een bedrijfstaxonomie bij ASML, gegeven door Daniel Canter. VOGIN-IP-lezing 18 april 2024

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ASML Taxonomy Adventure
Taxonomy Boot Camp 2023
Daniel Canter
KM Program Manager, Learning & KM, ASML
6th November, 2023

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Agenda
• About ASML
• The Story
• Project Approach
• Challenges, Successes, and Lessons Learned
• Painting the Future
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About ASML
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Chips are already the fabric of our modern world
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ASML makes the machines for making those chips
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• Lithography is the critical tool for producing
chips
• All major chipmakers use ASML’s
technology
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ASML at a glance
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Q2 2023 results
60%
growth in
2 yrs
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About ASML
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The Story
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What Next??
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10AREAS OF EXPERTISE
KM STRATEGY & DESIGN TAXONOMY & ONTOLOGY DESIGN
TECHNOLOGY SOLUTIONS AGILE, DESIGN THINKING, & FACILITATION
CONTENT & BRAND STRATEGY KNOWLEDGE GRAPHS, DATA MODELING, & AI
ENTERPRISE SEARCH INTEGRATED CHANGE MANAGEMENT
ENTERPRISE LEARNING CONTENT MANAGEMENT
HEADQUARTERED IN WASHINGTON, DC, USA
ESTABLISHED 2013 – OUR FOUNDERS AND PRINCIPALS HAVE BEEN PROVIDING KNOWLEDGE
MANAGEMENT CONSULTING TO GLOBAL CLIENTS FOR OVER 20 YEARS.
PRESENCE IN BRUSSELS, BELGIUM
EK At A Glance
STABLE CLIENT BASE

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ASML’s Taxonomy Journey
2022Q1
KM strategy defined
Taxonomy key need
2022Q2
RFP sent out
EK Hired
2022Q3
Project kickoff
Jan 2023
1st Version validated
Governance model
Mar-Jun 2023
Implementation planning
TOMS RFP
Jun-Nov 2023
Taxonomy enrichment
IT Purchasing
Nov 2023 on
Lets implement!
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Enable faster access to Learning and Knowledge content by using faceted search (e.g. by
topic, purpose, location, etc.).
Add a layer of findability and discoverability for employees to find organizational content
faster and more easily across content repositories, including the Intranet.
Implement a user-centric and scalable taxonomy to help organize, find, and discover content
and supports organization’s end-to-end KM approach.
Tag Learning and Knowledge content using terms from a customized taxonomy that
represents the content needs of groups to increase the accuracy of search.
Provide a customized experience for end users of the Intranet by targeting content to specific
audiences, ensuring that the right people have access to the right content when they need it.
Implement a governance model that helps maintain and evolve the taxonomy and aligns with
organization’s organizational culture and rapid growth.
ASML’s Taxonomy Needs

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Priority Personas and Use Cases for ASML
Engineer New Hire Office
Worker
Auto-tagging
Standardization of Vocabulary
Skill/Competency Tagging
& Learning Paths
Technical Content Findability,
document & content types
Personalized
Support a New Hire to find
relevant content.
Streamline User's experience
Develop an Employee’s skills
& performance
Improve search & discovery
of content, provide basis for
knowledg capture

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Priority Personas and Use Cases for ASML
Engineer New Hire Office
Worker
Auto-tagging
Standardization of Vocabulary
Skill/Competency Tagging
& Learning Paths
Technical Content Findability,
document & content types
Personalized
Support a New Hire to find
relevant content.
Streamline User's experience
Develop an Employee’s skills
& performance
Improve search & discovery
of content, provide basis for
knowledg capture

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ENTERPRISE KNOWLEDGE
ASML Priorities for TOMS Implementation
People to content
Content to people
People to people
FINDABILITY,
DICOVERABILITY
PUSH CONTENT
CONNECT PEOPLE
Relevant

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Project Approach
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Collaboration
The ability for stakeholders and users to work together in
terms of content creation and content management.
Includes the ability to co-author, co-edit, and work on a
document alongside other users at the same time.
Governance
The roles, responsibilities, processes, and
procedures necessary to ensure that an
organization’s content, taxonomy, information
architecture, and search continue to meet and
reflect evolving needs of the business and
users over the long term.
Taxonomy & Information
Architecture
Taxonomies are controlled vocabularies used to
describe or characterize explicit concepts of
information. Information architecture, alongside the
taxonomy, works to guide the standardization and
simplification of where and how content is stored and
tagged.
Change Management
Addresses modifying behavior,
impacting culture, and realizing ROI
while building internal capacity to
manage change. It places people at the
center of the process to make change
real and ensure it sticks.
Search
The best search experiences connect
people to information, information to
information, and people to people, and
address the foundational concepts in
search including action-oriented results,
faceting, semantic capabilities, analytics,
and governance.
Automation
The ability to reduce the
manual work associated with
tasks, processes, and
procedures through the creation
and application of technology.
Content & Document
Management
The strategies, methods, and tools
used to capture, manage, store, and
share content and documents in the
most optimal way possible.
Holistic Approach to Taxonomy
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Taxonomy Design Activities
Cross-organizational groups of
stakeholders shared their vision
of the taxonomy for ASML and
provided candidate metadata
fields and taxonomy use cases.
Workshops
Focus groups per business area
helped identify metadata fields
that were unique to their own
business area vs. applicable to
the organization as a whole.
Focus Groups
Interviews with key project
stakeholders and senior leaders
provided ASML’s short-term and
long-term vision for ASML’s
Taxonomy.
Interviews
Demos of content management
systems and learning
management systems allowed
the project team to align on the
organization’s content and
taxonomy strengths and gaps.
System Demos
A manual reviewing of individual
pieces of content (e.g. documents or
website pages) helped identify
patterns of content and possible
taxonomy terms.
Content Analysis
A “quick reference” list of past or
existing documents was helpful to
identify details about ASML’s current
and previous content management,
taxonomy, and search efforts.
Background
Documentation Review
The use of a text mining entity
extraction tool (PoolParty) helped
uncover the complexity of information
and identify new terms to seek, find,
and relate Intranet content.
Corpus Analysis
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Tree Test An online activity where
users were asked
questions in relation to
specific content items
while navigating through
one of the taxonomies.
This activity helped
validate the intuitiveness of
the taxonomy design.
Test
Tagging
In this activity, users were
asked to tag sample
content items with
selected areas from the
taxonomy design. This
activity provided insight
into the completeness and
intuitiveness of the
taxonomy design.
● Focus: Employee Support Topic Taxonomy
Three sets of validation activities were chosen to evaluate the completeness, usability, and
alignment of the starter taxonomy design over the course of a 5-week period
Validation
Discussion Facilitated meetings with
key stakeholders to view
the Enterprise Taxonomy
at a high-level encouraged
participants to discuss
potential areas for further
development or
refinement.
● Focus: Enterprise Taxonomy Overall
Taxonomy Validation Activities
Corpus
Analysis
The corpus analysis was
conducted with Intranet
content to seek candidate
terms and assess the
completeness of the Topic
taxonomy.
● Focus: Document Type and Employee
Support Topic Taxonomies
● Focus: Employee Support Topics Taxonomy
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TOMS
Taxonomists
Auto-tagging
Programmatically tagging content using taxonomy concepts and storing
concepts as metadata alongside content in source system.
Connector Cornerstone
Solution Architecture
Features:
Auto-tagging
Synonyms
Multi-Lingual Support
Connectivity (APIs)
Security

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The design included 13 taxonomies with 1,795 terms.
Validation activities included participation from 246 stakeholders from across the organization.
Taxonomy Design and Validation

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Learning
Content
Org Structure
Job Architecture
Product
Competences
Employee
This model unifies the 3 models developed with inputs from on site workshops and incorporates feedback from
validation sessions. There are 7 main areas of the starter ontology for ASML, shown below.
Legend
Ontology Design and Validation

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Challenges,
Successes, and
Lessons Learned
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Challenge 1: Stakeholder Management
Lots of very engaged stakeholders, all with ideas, none with any time
ASML is a network organization 
We all have a voice that wants to be heard.
Everyone that touched something to do with this
project became an interested party and needed
to be managed.
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Stakeholders
Engaged
Online Tree Test Validation
Activity Participants
84+ 246
1
ASML
project team
So, so, so many stakeholders…
• Different stakes
• Different levels of understanding
• Very limited time availability and lack of
meeting discipline
• Persistent need for rescheduling
Lessons Learned:
• Plan meetings weeks in advance
• Get whole teams involved, not just
representatives
• Have a well-organized stakeholder list
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Challenge 2: Input Volume
Limited scope of Learning & knowledge Management
36 systems + SharePoint + Teams + Intranet
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Taxonomy Sources
1.3k
+
ASML Taxonomy Terms Analyzed
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System Demos & Taxonomy
Walkthroughs
ASML asked itself:
• Where to start?
• What to focus on?
• How do we create something useful from
all this?
Lessons Learned:
• Limit scope to 1-2 systems that impact a wide
audience
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Challenge 3: How to choose the management system?
There was no internal understanding of such systems beyond data management & editorial
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What is a Taxonomy Ontology Management System?
How does this help?
How does it work?
Why is it different from what we have?
What will it do for the organization?
Lessons Learned:
What do we know is needed:
1. Do the research on your repositories
2. Spend time working out the elements you need
3. Work VERY closely with IT Architecture teams
4. Connect closely with your data office
5. Get the CIO involved, make the connections
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Challenge 4: Lack of Understanding of Future Possibilities
The people that understand Taxonomy are very thinly spread, so lots of questions
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How does this help?
What is Taxonomy for?
How do we use it?
How does it relate to search and findability?
Is it required for A.I. to work?
Lessons Learned:
• Involve the business early on to discuss “what
can we do with a Taxonomy once we have one?”,
“which advanced taxonomy use cases can we
support in the future?”
• Involve the technical team to identify technology
capabilities and limitations and align with the
business needs.
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Painting the Future
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FOLKSONOMY CONTROLLED
LIST
TAXONOMY ONTOLOGY KNOWLEDGE
GRAPH
ARTIFICIAL
INTELLIGENCE
Free-text tags. List of predefined
terms. Improves
consistency.
Predefined terms &
synonyms.
Hierarchical
relationships.
Improves
consistency. Allows
for parent/child
content
relationships.
Predefined classes
& properties.
Expanded
relationships types.
Increased
expressiveness.
Semantics.
Inference.
Capture related
data. Integration of
structured and
unstructured
information. Linked
data store.
Architecture and
data models to
enable machine
learning and other AI
capabilities.
Drive efficient and
intelligent data and
information
management
solutions.
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Recommender Systems
Data Management &
Quality
Auto-tagging
Taxonomy & Ontology
Development
Standardization and
Dereferencing
Natural Language and
Semantic Search
Data Visualization and
Reporting Dashboard
Data Governance
@EKCONSULTING
Future Enterprise Applications and Use Cases
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• FIV Taxonomy Design and
Validation
• Starter Ontology Design &
Validation
• Taxonomy Governance Model
Creation & Validation
• Solution Architecture & TOMS
Selection Recommendations
• TOMS Implementation and
Integration Roadmap
• Taxonomy Enrichment Guidance
• Analysis for Pilot Taxonomy
Implementation in the Intranet
• TOMS Implementation and
FIV Taxonomy upload
• TOMS Integration with
ASML’s priority systems,
(Intranet and SharePoint)
• TOMS Role-based Training
and System Specific Training
• Expand Taxonomy Design to
Additional Domains
• TOMS Integration with
additional ASML systems
• FIV Knowledge Graph
• Advanced taxonomy and
ontology applications for AI
solutions (chatbots and
content recommender
systems).
Completed
Design Phase
In Progress Future
Implementation Phase Scale Phase

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Thanks! Daniel Canter
KM Program Manager, ASML