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The Role of Taxonomy and Ontology
in Semantic Layers
Heather Hedden
Senior Consultant
Enterprise Knowledge, LLC
Webinar
April 16, 2024
10AREAS OF EXPERTISE
KM STRATEGY & DESIGN TAXONOMY & ONTOLOGY DESIGN
TECHNOLOGY SOLUTIONS AGILE, DESIGN THINKING, & FACILITATION
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Enterprise Knowledge at a Glance
ENTERPRISE KNOWLEDGE
Outline
Introduction to
Taxonomies
Introduction
to Ontologies
Introduction to
the Semantic
Layer
What is a Taxonomy?
A knowledge organization
system that is…
1. Controlled:
A kind of controlled
vocabulary, based on
unambiguous concepts,
not just words
(things, not strings).
2. Organized:
Concepts are organized in
a structure of hierarchies,
categories, or facets to
make them easier to find
and understand.
Controlled
Organized
What is a Taxonomy For?
⬢ Concepts are used to
tag/categorize
content to make
finding and
retrieving specific
content easier.
⬢ Supporting better
findability than
search alone.
⬢ The taxonomy is an
intermediary that
links users to the
desired content.
Taxonomy
implementation:
focus on
organized
Taxonomy
implementation:
focus on
controlled
What are Taxonomy Uses?
What you can do with a taxonomy…
⬢ Consistent tagging: Enable comprehensive and accurate content retrieval
⬢ Normalization: Bring together different names, localizations, languages for concepts
⬢ Search: Find content about… (search strings match tagged taxonomy concepts)
⬢ Topic browse: Explore subjects arranged in a hierarchy and then content on the subject
⬢ Faceted (filtering/refining) search: Find content meeting a combination of basic
criteria
⬢ Discovery: Find other content tagged with same concepts as tagged to found content;
explore broader, narrower, and (sometimes) related taxonomy topics
⬢ Content curation: Create feeds or alerts based on pre-set search terms
⬢ Metadata management: Support identification, comparison, mapping, analysis, etc.
What is an Ontology?
An ontology is a model of knowledge domain.
A structured set of entities, relationships and attributes
in a subject domain, with semantic expressiveness
⬢ A form of knowledge representation.
⬢ In addition to knowledge organization.
⬢ A set of precise descriptive statements about a particular domain.
⬢ Statements as subject-predicate-object are expressed as triples.
⬢ A formal naming and definition of the types, properties and interrelationships of
entities in a particular domain.
⬢ Classes, custom semantic relationships, custom attributes.
⬢ A more abstract layer in describing a knowledge organization system.
⬢ Overlays and connects to a taxonomy/controlled vocabularies with added semantics.
⬢ Based on W3C Semantic Web standards: RDF, RDF-Schema, and OWL.
Class: Company
Individual: Widgets, Inc.
Relation: Has contract with
What is an Ontology?
An ontology can be
applied to a taxonomy
as a semantic layer,
defining classes,
and adding
semantic relations
and attributes.
What is an Ontology?
An ontology can be
applied to a taxonomy
as a semantic layer,
defining classes,
and adding
semantic relations
and attributes.
What are Taxonomy + Ontology Uses?
What you cannot do with a taxonomy alone, but can with an added ontology
⬢ Model complex interrelationships: e.g. in-product approval or supply chain processes,
connect to content
⬢ Perform complex multi-part searches: e.g. find contacts in a specific location, who are
employed by companies which belong to certain industries
⬢ Search on more specific criteria that vary based on category (class)
⬢ Explore explicit relationships between concepts (not just broader, narrower, related)
⬢ Visualize of concepts and semantic relationships
⬢ Perform reasoning and inferencing across data
⬢ Search across datasets, not just search for content
⬢ Connect across siloed content and data repositories across the enterprise
What is an Ontology For?
Applications that use ontologies
Advanced semantic search
Business analytics tools
Insight engines
Recommendation systems
Intelligent chatbots
Natural language question-answering
An ontology can also link
across multiple taxonomies
and other controlled
vocabularies.
An ontology can also
link across multiple
taxonomies and other
controlled
vocabularies,
providing a means of
connecting them.
Why a Semantic Layer?
⬢ A taxonomy or ontology is more useful if not siloed within a single application.
⬢ A taxonomy + ontology can help connect across siloed content and data repositories
across the enterprise.
⬢ This is done through a Semantic Layer approach and architecture.
Problems:
● Data silos
● Heterogeneous data sources
● Mix of unstructured and structured data
● Same things with different names
● Localized meanings for the same thing
Solutions:
● Sharing data and content
● Semantic links
● Unified vocabulary
● Unified application view
Causing:
● Inefficiencies
● Missed opportunities
● Poor decisions
Provided by:
● Semantic Layer
Achieving:
● Enterprise intelligence
ENTERPRISE KNOWLEDGE
Applications
Enterprise
CMS
Search UI
Enterprise
CMS
Taxonomy
& Metadata
Enterprise
CMS
Content
Website
Search UI
Web CMS
Taxonomy
& Metadata
Web CMS
Content
Intranet
Search UI
Intranet
Taxonomy
& Metadata
Intranet
Content
Ecommerce
Search UI
PIM
Taxonomy
& Metadata
PIM Product
Data
CRM
Search UI
CRM
Taxonomy
& Metadata
CRM
Contacts
Data
Siloed Content/Data and Applications
Semantic
Layer
Data &
Content
Sources
Presentation
Layer
ENTERPRISE KNOWLEDGE
Semantic
Layer
APIs ETL
Application
Search
Research &
Analytics
Recommendations
& Chatbots
Admin &
Governance
Knowledge Graph
Metadata Service
Business Glossary
Ontology
Graph Data Schema
Taxonomy
Content Management
Systems
Data Lake / Data
Warehouse
Subscriptions External Sources
Data &
Content
Sources
Presentation
Layer
APIs
Semantic Layer: Connecting Across Silos
Shared
Drives
What is the Semantic Layer?
A Semantic Layer is a
standardized framework
that organizes and abstracts
organizational knowledge and
data (structured, unstructured,
semi-structured)
and serves as a connector for all
organizational knowledge assets.
A Semantic Layer enables data
federation and virtualization of
semantic labels or rules (e.g.
taxonomies/ business glossaries
or ontologies) to capture and
connect data based on business
or domain meaning and value.
Semantic Layer Components
Metadata
Describe, standardize,
catalog
Consistent data type properties
Information Architecture
& Taxonomy
Name, organize, architect
Concepts structured for findability
Business Glossary
Identify, define, align
Terms with definitions for shared
understanding
Ontology
& Knowledge Graph
Relate, map, contextualize
Model of a knowledge domain to
connect, analyze, and infer
Content
Tag, classify, find, retrieve
Connecting to knowledge, data,
and information assets
Taxonomy & Ontology in the Semantic Layer
Connected taxonomy approaches:
1. A single enterprise taxonomy
⬢ Different concepts exposed in different applications as needed (via SKOS collections)
⬢ Different labels for the same concepts managed with label properties (via SKOS-XL)
2. Frontend application taxonomy(s) linked to repository taxonomies (via SKOS mapping
relations)
3. A master hub taxonomy including all concepts from all taxonomies, linked to all other
taxonomies (via SKOS mapping relations)
Connected ontology approaches:
1. A single enterprise ontology
2. An enterprise ontology that links across taxonomies and other controlled vocabularies
3. Multiple varied custom schemes derived from a single enterprise ontology
Problems a Semantic Layer Addresses
INCONSISTENT METADATA
Inconsistently applied metadata or uncontrolled
metadata values make it difficult or impossible to
connect data across systems, compounding
inefficiencies
NON-INTUITIVE USER INTERACTIONS
Technical expertise required to interact with systems
prevents broader access to data; systems are not
user-friendly and require extensive training
INEFFICIENT DATA ANALYSIS PROCESSES
Manual analysis and reporting can take weeks for
one person to collect from multiple disconnected
sources to capture insights at an enterprise level
and make better business decisions
POOR DATA QUALITY & GOVERNANCE
No way to produce repeatable data reports with
lineage tracking to comply with regulatory
requirements
VENDOR LOCK
Data encoded in proprietary, vendor specific formats
prevents organizations from being able to evolve
their technology ecosystem as needs change
AI HALLUCINATIONS
Unreliable AI responses due to lack of context /
alignment with business meaning of data, producing
hallucinations and introducing challenges in
following how AI decisions were reached
Implementing the Semantic Layer
Possible approaches
1. A Metadata-First Logical Architecture:
Using Enterprise Semantic Layer Solutions
2. Built-for-Purpose Architecture:
Individual Tools with Semantic Capabilities
3. A Centralized Architecture:
Within an Enterprise Data Warehouse or
Data Lake
Metadata-First Logical Architecture
A Metadata-First Logical Architecture
⬢ Using an enterprise semantic layer solution.
⬢ Creating a logical layer that abstracts
the underlying data sources by focusing
on metadata.
⬢ The most common approach.
⬢ Progress Semaphore is a tool for this
enterprise semantic layer solution approach.
Resources
⬢ “What is a Semantic Layer (Components and Enterprise Applications,” (February 1, 2024) Lulit
Tesfaye, Enterprise Knowledge Blog.
⬢ “The Top 5 Reasons for a Semantic Layer” (February 14, 2024) Joe Hilger, Enterprise Knowledge
Blog.
⬢ "The Top 3 Ways to Implement a Semantic Layer" (March 12, 2024) Lulit Tesfaye, Enterprise
Knowledge Blog.
⬢ “What Every CEO Needs to Know About Semantic Layers” (February 29, 2024) by Zach Wahl,
Enterprise Knowledge Blog.
⬢ “What isn’t a Semantic Layer” (February 23, 2024) by Sara Nash, Enterprise Knowledge Blog.
⬢ “What is a Semantic Architecture and How do I Build One?” White Paper (April 2, 2020) by Lulit
Tesfaye, Enterprise Knowledge
⬢ “The Importance of a Semantic Layer in a Knowledge Management Technology Suite” (May 27,
2021), Enterprise Knowledge Blog.
⬢ "Defining the Semantic Layer Webinar" (March 1, 2024) webinar recording
⬢ "KM Trends – Semantic Layer" (February 8, 2024) Zach Wahl, podcast recording
enterprise-knowledge.com/knowledge-base
Q&A
Thank you for listening.
Questions?
Heather Hedden
Senior Consultant
Enterprise Knowledge, LLC
www.enterprise-knowledge.com
hhedden@enterprise-knowledge.com
www.linkedin.com/in/hedden

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The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf

  • 1. The Role of Taxonomy and Ontology in Semantic Layers Heather Hedden Senior Consultant Enterprise Knowledge, LLC Webinar April 16, 2024
  • 2. 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 80 + EXPERT CONSULTANTS HEADQUARTERED IN WASHINGTON, DC, USA ESTABLISHED 2013 – OUR FOUNDERS AND PRINCIPALS HAVE BEEN PROVIDING KNOWLEDGE MANAGEMENT CONSULTING TO GLOBAL CLIENTS FOR OVER 20 YEARS. KMWORLD’S 100 COMPANIES THAT MATTER IN KM (2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024) TOP 50 TRAILBLAZERS IN AI (2020, 2021, 2022) CIO REVIEW’S 20 MOST PROMISING KM SOLUTION PROVIDERS (2016) INC MAGAZINE #2,343 OF THE 5000 FASTEST GROWING COMPANIES (2021) #2,574 OF THE 5000 FASTEST GROWING COMPANIES (2020) #2,411 OF THE 5000 FASTEST GROWING COMPANIES (2019) #1,289 OF THE 5000 FASTEST GROWING COMPANIES (2018) INC MAGAZINE BEST WORKPLACES (2018, 2019, 2021, 2022) WASHINGTONIAN MAGAZINE’S TOP 50 GREAT PLACES TO WORK (2017) WASHINGTON BUSINESS JOURNAL’S BEST PLACES TO WORK (2017, 2018, 2019, 2020) ARLINGTON ECONOMIC DEVELOPMENT’S FAST FOUR AWARD – FASTEST GROWING COMPANY (2016) VIRGINIA CHAMBER OF COMMERCE’S FANTASTIC 50 AWARD – FASTEST GROWING COMPANY (2019, 2020) AWARD-WINNING CONSULTANCY PRESENCE IN BRUSSELS, BELGIUM STABLE CLIENT BASE Enterprise Knowledge at a Glance
  • 3. ENTERPRISE KNOWLEDGE Outline Introduction to Taxonomies Introduction to Ontologies Introduction to the Semantic Layer
  • 4. What is a Taxonomy? A knowledge organization system that is… 1. Controlled: A kind of controlled vocabulary, based on unambiguous concepts, not just words (things, not strings). 2. Organized: Concepts are organized in a structure of hierarchies, categories, or facets to make them easier to find and understand. Controlled Organized
  • 5. What is a Taxonomy For? ⬢ Concepts are used to tag/categorize content to make finding and retrieving specific content easier. ⬢ Supporting better findability than search alone. ⬢ The taxonomy is an intermediary that links users to the desired content. Taxonomy implementation: focus on organized Taxonomy implementation: focus on controlled
  • 6. What are Taxonomy Uses? What you can do with a taxonomy… ⬢ Consistent tagging: Enable comprehensive and accurate content retrieval ⬢ Normalization: Bring together different names, localizations, languages for concepts ⬢ Search: Find content about… (search strings match tagged taxonomy concepts) ⬢ Topic browse: Explore subjects arranged in a hierarchy and then content on the subject ⬢ Faceted (filtering/refining) search: Find content meeting a combination of basic criteria ⬢ Discovery: Find other content tagged with same concepts as tagged to found content; explore broader, narrower, and (sometimes) related taxonomy topics ⬢ Content curation: Create feeds or alerts based on pre-set search terms ⬢ Metadata management: Support identification, comparison, mapping, analysis, etc.
  • 7. What is an Ontology? An ontology is a model of knowledge domain. A structured set of entities, relationships and attributes in a subject domain, with semantic expressiveness ⬢ A form of knowledge representation. ⬢ In addition to knowledge organization. ⬢ A set of precise descriptive statements about a particular domain. ⬢ Statements as subject-predicate-object are expressed as triples. ⬢ A formal naming and definition of the types, properties and interrelationships of entities in a particular domain. ⬢ Classes, custom semantic relationships, custom attributes. ⬢ A more abstract layer in describing a knowledge organization system. ⬢ Overlays and connects to a taxonomy/controlled vocabularies with added semantics. ⬢ Based on W3C Semantic Web standards: RDF, RDF-Schema, and OWL. Class: Company Individual: Widgets, Inc. Relation: Has contract with
  • 8. What is an Ontology? An ontology can be applied to a taxonomy as a semantic layer, defining classes, and adding semantic relations and attributes.
  • 9. What is an Ontology? An ontology can be applied to a taxonomy as a semantic layer, defining classes, and adding semantic relations and attributes.
  • 10. What are Taxonomy + Ontology Uses? What you cannot do with a taxonomy alone, but can with an added ontology ⬢ Model complex interrelationships: e.g. in-product approval or supply chain processes, connect to content ⬢ Perform complex multi-part searches: e.g. find contacts in a specific location, who are employed by companies which belong to certain industries ⬢ Search on more specific criteria that vary based on category (class) ⬢ Explore explicit relationships between concepts (not just broader, narrower, related) ⬢ Visualize of concepts and semantic relationships ⬢ Perform reasoning and inferencing across data ⬢ Search across datasets, not just search for content ⬢ Connect across siloed content and data repositories across the enterprise
  • 11. What is an Ontology For? Applications that use ontologies Advanced semantic search Business analytics tools Insight engines Recommendation systems Intelligent chatbots Natural language question-answering An ontology can also link across multiple taxonomies and other controlled vocabularies. An ontology can also link across multiple taxonomies and other controlled vocabularies, providing a means of connecting them.
  • 12. Why a Semantic Layer? ⬢ A taxonomy or ontology is more useful if not siloed within a single application. ⬢ A taxonomy + ontology can help connect across siloed content and data repositories across the enterprise. ⬢ This is done through a Semantic Layer approach and architecture. Problems: ● Data silos ● Heterogeneous data sources ● Mix of unstructured and structured data ● Same things with different names ● Localized meanings for the same thing Solutions: ● Sharing data and content ● Semantic links ● Unified vocabulary ● Unified application view Causing: ● Inefficiencies ● Missed opportunities ● Poor decisions Provided by: ● Semantic Layer Achieving: ● Enterprise intelligence
  • 13. ENTERPRISE KNOWLEDGE Applications Enterprise CMS Search UI Enterprise CMS Taxonomy & Metadata Enterprise CMS Content Website Search UI Web CMS Taxonomy & Metadata Web CMS Content Intranet Search UI Intranet Taxonomy & Metadata Intranet Content Ecommerce Search UI PIM Taxonomy & Metadata PIM Product Data CRM Search UI CRM Taxonomy & Metadata CRM Contacts Data Siloed Content/Data and Applications Semantic Layer Data & Content Sources Presentation Layer
  • 14. ENTERPRISE KNOWLEDGE Semantic Layer APIs ETL Application Search Research & Analytics Recommendations & Chatbots Admin & Governance Knowledge Graph Metadata Service Business Glossary Ontology Graph Data Schema Taxonomy Content Management Systems Data Lake / Data Warehouse Subscriptions External Sources Data & Content Sources Presentation Layer APIs Semantic Layer: Connecting Across Silos Shared Drives
  • 15. What is the Semantic Layer? A Semantic Layer is a standardized framework that organizes and abstracts organizational knowledge and data (structured, unstructured, semi-structured) and serves as a connector for all organizational knowledge assets. A Semantic Layer enables data federation and virtualization of semantic labels or rules (e.g. taxonomies/ business glossaries or ontologies) to capture and connect data based on business or domain meaning and value.
  • 16. Semantic Layer Components Metadata Describe, standardize, catalog Consistent data type properties Information Architecture & Taxonomy Name, organize, architect Concepts structured for findability Business Glossary Identify, define, align Terms with definitions for shared understanding Ontology & Knowledge Graph Relate, map, contextualize Model of a knowledge domain to connect, analyze, and infer Content Tag, classify, find, retrieve Connecting to knowledge, data, and information assets
  • 17. Taxonomy & Ontology in the Semantic Layer Connected taxonomy approaches: 1. A single enterprise taxonomy ⬢ Different concepts exposed in different applications as needed (via SKOS collections) ⬢ Different labels for the same concepts managed with label properties (via SKOS-XL) 2. Frontend application taxonomy(s) linked to repository taxonomies (via SKOS mapping relations) 3. A master hub taxonomy including all concepts from all taxonomies, linked to all other taxonomies (via SKOS mapping relations) Connected ontology approaches: 1. A single enterprise ontology 2. An enterprise ontology that links across taxonomies and other controlled vocabularies 3. Multiple varied custom schemes derived from a single enterprise ontology
  • 18. Problems a Semantic Layer Addresses INCONSISTENT METADATA Inconsistently applied metadata or uncontrolled metadata values make it difficult or impossible to connect data across systems, compounding inefficiencies NON-INTUITIVE USER INTERACTIONS Technical expertise required to interact with systems prevents broader access to data; systems are not user-friendly and require extensive training INEFFICIENT DATA ANALYSIS PROCESSES Manual analysis and reporting can take weeks for one person to collect from multiple disconnected sources to capture insights at an enterprise level and make better business decisions POOR DATA QUALITY & GOVERNANCE No way to produce repeatable data reports with lineage tracking to comply with regulatory requirements VENDOR LOCK Data encoded in proprietary, vendor specific formats prevents organizations from being able to evolve their technology ecosystem as needs change AI HALLUCINATIONS Unreliable AI responses due to lack of context / alignment with business meaning of data, producing hallucinations and introducing challenges in following how AI decisions were reached
  • 19. Implementing the Semantic Layer Possible approaches 1. A Metadata-First Logical Architecture: Using Enterprise Semantic Layer Solutions 2. Built-for-Purpose Architecture: Individual Tools with Semantic Capabilities 3. A Centralized Architecture: Within an Enterprise Data Warehouse or Data Lake Metadata-First Logical Architecture A Metadata-First Logical Architecture ⬢ Using an enterprise semantic layer solution. ⬢ Creating a logical layer that abstracts the underlying data sources by focusing on metadata. ⬢ The most common approach. ⬢ Progress Semaphore is a tool for this enterprise semantic layer solution approach.
  • 20. Resources ⬢ “What is a Semantic Layer (Components and Enterprise Applications,” (February 1, 2024) Lulit Tesfaye, Enterprise Knowledge Blog. ⬢ “The Top 5 Reasons for a Semantic Layer” (February 14, 2024) Joe Hilger, Enterprise Knowledge Blog. ⬢ "The Top 3 Ways to Implement a Semantic Layer" (March 12, 2024) Lulit Tesfaye, Enterprise Knowledge Blog. ⬢ “What Every CEO Needs to Know About Semantic Layers” (February 29, 2024) by Zach Wahl, Enterprise Knowledge Blog. ⬢ “What isn’t a Semantic Layer” (February 23, 2024) by Sara Nash, Enterprise Knowledge Blog. ⬢ “What is a Semantic Architecture and How do I Build One?” White Paper (April 2, 2020) by Lulit Tesfaye, Enterprise Knowledge ⬢ “The Importance of a Semantic Layer in a Knowledge Management Technology Suite” (May 27, 2021), Enterprise Knowledge Blog. ⬢ "Defining the Semantic Layer Webinar" (March 1, 2024) webinar recording ⬢ "KM Trends – Semantic Layer" (February 8, 2024) Zach Wahl, podcast recording enterprise-knowledge.com/knowledge-base
  • 21. Q&A Thank you for listening. Questions? Heather Hedden Senior Consultant Enterprise Knowledge, LLC www.enterprise-knowledge.com hhedden@enterprise-knowledge.com www.linkedin.com/in/hedden