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Scaling Knowledge Graph
Architectures with AI
Sara Nash and Urmi Majumder
Text Analytics 2023
ENTERPRISE KNOWLEDGE
Outline
Introduction Knowledge
Graphs
Solutions
Architecture
Maturity
Spectrum
What You Will Learn
⬢ How AI supports the creation and growth of Knowledge Graphs
⬢ Different approaches for entity and relationship extraction
depending on Enterprise AI maturity
⬢ Key considerations to incorporate AI capabilities into a Knowledge
Graph development pipeline
⬢ Serves as implementation lead for knowledge graphs - ranging
from early design and prototyping to enterprise solutions
⬢ Expert in Knowledge Graph and Semantic Technologies
⬢ Established standards in design and delivery of semantic
recommender solutions
SARA
PRINCIPAL CONSULTANT, ENTERPRISE KNOWLEDGE
NASH
URMI
PRINCIPAL CONSULTANT, ENTERPRISE KNOWLEDGE
MAJUMDER
⬢ Expert in system architecture, design, and implementation
of semantic enterprise solutions
⬢ Leads the development of technical solutions in support of
a wide variety of both federal and commercial clients
ENTERPRISE KNOWLEDGE
ENTERPRISE KNOWLEDGE
A knowledge graph is a specialized
graph of the things we want to
describe and how they are related.
● Standardize data entities and
enrich data with context.
● Can be expanded by leveraging
various approaches, including
AI-driven entity and
relationship extraction.
Most of the data in our world is
unstructured.
Unstructured data:
1) Has no metadata
2) Can’t be captured neatly in
structured formats like XML, JSON,
or relational databases; and,
3) Lacks standardization, which
prevents establishing uniform
processes for analysis.
The Challenge The Knowledge Graph
Creating Knowledge from Unstructured Content
There is a vast amount of information embedded in documents, reports, records, process flow
diagrams, and more. There is an opportunity to extract this knowledge and make
meaningful connections to accelerate knowledge discovery across teams.
Extract Knowledge from Content Organize Knowledge in Logical Structure Get Insights from Data
ABC
Material
XYZ
Product
Process 1
Process Step
E1
Experiment
F2
Material
isInput
isOutput
E0
Experiment
isInput isInput
creates
What Materials were used
to make XYZ?
● ABC was used in Process 1
What are the experiments
in which ABC was used?
● E0
● E1
Which experiment of ABC
was used to make F2?
● E1
ENTERPRISE KNOWLEDGE
Natural Language
Processing (NLP)
model leveraged to
build a Knowledge
Graph (KG) for
providing coherent
and relevant learning
content
recommendations.
ML model was used to
facilitate KG
generating dynamic
automated
regulatory reporting,
and expediting
research and
publication processes.
Learning Enablement Safety Standards Regulatory Reporting and More
Discovery Analysis Research
Machine Learning
(ML) model was
trained to allow KG to
facilitate thorough
analyses of possible
risks, and help
planners plan the
best safety measures
for mitigation.
● Product Marketing
● E-Commerce
● Content Cleanup
● Data Discovery in
Research
Top Graph Use Cases: https://enterprise-knowledge.com/top-graph-use-cases-and-enterprise-applications-with-real-world-examples/
Success Stories
Source Data and
Content
Taxonomy/
Schema Storage
Entity and
Relationship
Extraction
Enterprise
Content and Data
Dedicated
Taxonomy/Ontology
Management System
Auto-tagging and/or
Extraction of Key
Knowledge
Enriched Content
Storage
Persistent Graph
Storage
Data Orchestration
Front End
Visualization /
UI
API
AI
Search
Chatbots/
Q&A
Data Visualization
and Reporting
Recommender
Systems
Solutions Architecture for Scalable Knowledge Graphs
Source Data and
Content
Taxonomy/
Schema Storage
Enterprise
Content and Data
Dedicated
Taxonomy/Ontology
Management System
Enriched Content
Storage
Persistent Graph
Storage
Data Orchestration
Front End
Visualization /
UI
API
Search
Chatbots/
Q&A
Data Visualization
and Reporting
Recommender
Systems
AI accelerates extracting entities and relationships at
scale from unstructured enterprise data. This is
increasingly possible due to advances in the natural
language processing space.
Entity and
Relationship
Extraction
Auto-tagging and/or
Extraction of Key
Knowledge
AI
Solutions Architecture for Scalable Knowledge Graphs
Transformational
Institutional
Unaware of
how AI is being
adopted across
organizations.
Hopeful about
the promise of
AI and its
impact on
business.
Some AI/ML
models are in
use for specific
use cases.
AI solutions are
supporting
shared use
cases across the
organization.
AI is part of
business DNA,
transforming
infrastructure
and processes
to improve
efficiency while
optimizing
costs.
Operational
Experimental
Not Ready
(Pre-AI)
ENTERPRISE KNOWLEDGE
AI Maturity Spectrum for the Enterprise
…with
Automated
Monitoring
and Retraining
Entity Extraction Maturity Spectrum
Regular
Expression
Based
(RegEx)
Auto
Classification
Custom ML
Model …for
Entity
Extraction
…with Active
Learning
Definition
Taxonomy driven
categorization of
content
Definition
Traditional supervised
learning approach for
text classification
Considerations
Highly dependent on
training data
Definition
Model is re-trained
periodically based on
human feedback
Considerations
Increased text
classification accuracy
Definition
Model is automatically
re-trained, tested, and
deployed
Considerations
Recommended for
large orgs with
established DataOps
processes
Definition
Use patterns of
characters and
operators to match
text
Considerations
Requires explicit
definition of rules,
and may lead to false
positives
Considerations
Limited to the
terms defined in the
taxonomy
Transformational
Institutional
Operational
Experimental
Not Ready
(Pre-AI)
RegEx Rule: Knowledge Graph
University is a Service because
[*]University is a Service.
This content on EK’s site is rich with knowledge
that can be extracted through different
approaches.
Auto-Classification Rule:
● EKGU is a synonym for Enterprise
Knowledge Graph University and this
article is about EKGU.
● Information Analyst is a Role, so
Information Analyst may take EKGU
Custom ML Model
● SPARQL and SHACL are
frameworks
● Taxonomy, ontology, and Knowledge
Graphs are semantic models
● Graph database is a tool
Entity Extraction in Action
…with
Automated
Monitoring
and Retraining
Source
Schema
Based
Rule Based
Custom ML
Model …for
Relationship
Extraction
…with Active
Learning
Definition
Custom rule set,
borrowing relationships
from standard formats
Considerations
Relies on maintaining
rules and may lead to
false positives
Definition
Traditional supervised
learning for classifying
text between two
entities
Considerations
Highly dependent on
training data
Relationship Extraction Maturity Spectrum
Definition
Model is automatically
re-trained, tested, and
deployed
Considerations
Recommended for
large orgs with
established DataOps
processes
Definition
Model is re-trained
periodically based on
human feedback
Considerations
Increased text
classification accuracy
Definition
Exploit the schema
(JSON, XML, etc.) of the
source system
Considerations
Requires explicit
mapping in data source
between entities to
assign relationships
Transformational
Institutional
Operational
Experimental
Not Ready
(Pre-AI)
AI Maturity Spectrum for the Enterprise Revisited
Transformational
Institutional
Operational
Experimental
Not Ready
(Pre-AI)
ENTERPRISE KNOWLEDGE
Use pattern
matching for
deterministic
entity and
relationship
extraction
Design a
starter
taxonomy &
use it for
taxonomy
driven graph
instantiation
Use pre-trained
ML models for
probabilistic
entity and
relationship
extraction
Fine-tune
pretrained ML
models based
on SME
feedback to
update graph
Monitor
information
extraction
quality to
automatically
retrain ML
model
Be iterative Start small, then iteratively refine and expand AI
integration.
Involve your SMEs SMEs can help validate AI performance and provide
feedback, ensuring accurate and relevant
improvements.
Define consumer-
facing use cases
Use cases should address pain points or challenges
faced by your target audience.
Invest in quality Invest in establishing robust AI models and
structured content and data that align with your use
cases.
In order to best incorporate AI capabilities into your Knowledge Graph
pipeline, there are several key factors to consider:
Key Considerations
ENTERPRISE KNOWLEDGE
Any Questions?
Thank you for listening.
We are happy to take any
questions at this time.
Sara Nash
snash@enterprise-knowledge.com
www.linkedin.com/in/sara-g-nash/
Urmi Majumder
umajumder@enterprise-knowledge.com
www.linkedin.com/in/urmim/
How prepared is your organization
for AI? Take EK’s AI Maturity
Assessment:
https://s.enterprise-knowledge.com/ekaiassessment

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Scaling Knowledge Graph Architectures with AI

  • 1. Scaling Knowledge Graph Architectures with AI Sara Nash and Urmi Majumder Text Analytics 2023
  • 2. ENTERPRISE KNOWLEDGE Outline Introduction Knowledge Graphs Solutions Architecture Maturity Spectrum What You Will Learn ⬢ How AI supports the creation and growth of Knowledge Graphs ⬢ Different approaches for entity and relationship extraction depending on Enterprise AI maturity ⬢ Key considerations to incorporate AI capabilities into a Knowledge Graph development pipeline
  • 3. ⬢ Serves as implementation lead for knowledge graphs - ranging from early design and prototyping to enterprise solutions ⬢ Expert in Knowledge Graph and Semantic Technologies ⬢ Established standards in design and delivery of semantic recommender solutions SARA PRINCIPAL CONSULTANT, ENTERPRISE KNOWLEDGE NASH URMI PRINCIPAL CONSULTANT, ENTERPRISE KNOWLEDGE MAJUMDER ⬢ Expert in system architecture, design, and implementation of semantic enterprise solutions ⬢ Leads the development of technical solutions in support of a wide variety of both federal and commercial clients ENTERPRISE KNOWLEDGE
  • 4. ENTERPRISE KNOWLEDGE A knowledge graph is a specialized graph of the things we want to describe and how they are related. ● Standardize data entities and enrich data with context. ● Can be expanded by leveraging various approaches, including AI-driven entity and relationship extraction. Most of the data in our world is unstructured. Unstructured data: 1) Has no metadata 2) Can’t be captured neatly in structured formats like XML, JSON, or relational databases; and, 3) Lacks standardization, which prevents establishing uniform processes for analysis. The Challenge The Knowledge Graph
  • 5. Creating Knowledge from Unstructured Content There is a vast amount of information embedded in documents, reports, records, process flow diagrams, and more. There is an opportunity to extract this knowledge and make meaningful connections to accelerate knowledge discovery across teams. Extract Knowledge from Content Organize Knowledge in Logical Structure Get Insights from Data ABC Material XYZ Product Process 1 Process Step E1 Experiment F2 Material isInput isOutput E0 Experiment isInput isInput creates What Materials were used to make XYZ? ● ABC was used in Process 1 What are the experiments in which ABC was used? ● E0 ● E1 Which experiment of ABC was used to make F2? ● E1
  • 6. ENTERPRISE KNOWLEDGE Natural Language Processing (NLP) model leveraged to build a Knowledge Graph (KG) for providing coherent and relevant learning content recommendations. ML model was used to facilitate KG generating dynamic automated regulatory reporting, and expediting research and publication processes. Learning Enablement Safety Standards Regulatory Reporting and More Discovery Analysis Research Machine Learning (ML) model was trained to allow KG to facilitate thorough analyses of possible risks, and help planners plan the best safety measures for mitigation. ● Product Marketing ● E-Commerce ● Content Cleanup ● Data Discovery in Research Top Graph Use Cases: https://enterprise-knowledge.com/top-graph-use-cases-and-enterprise-applications-with-real-world-examples/ Success Stories
  • 7. Source Data and Content Taxonomy/ Schema Storage Entity and Relationship Extraction Enterprise Content and Data Dedicated Taxonomy/Ontology Management System Auto-tagging and/or Extraction of Key Knowledge Enriched Content Storage Persistent Graph Storage Data Orchestration Front End Visualization / UI API AI Search Chatbots/ Q&A Data Visualization and Reporting Recommender Systems Solutions Architecture for Scalable Knowledge Graphs
  • 8. Source Data and Content Taxonomy/ Schema Storage Enterprise Content and Data Dedicated Taxonomy/Ontology Management System Enriched Content Storage Persistent Graph Storage Data Orchestration Front End Visualization / UI API Search Chatbots/ Q&A Data Visualization and Reporting Recommender Systems AI accelerates extracting entities and relationships at scale from unstructured enterprise data. This is increasingly possible due to advances in the natural language processing space. Entity and Relationship Extraction Auto-tagging and/or Extraction of Key Knowledge AI Solutions Architecture for Scalable Knowledge Graphs
  • 9. Transformational Institutional Unaware of how AI is being adopted across organizations. Hopeful about the promise of AI and its impact on business. Some AI/ML models are in use for specific use cases. AI solutions are supporting shared use cases across the organization. AI is part of business DNA, transforming infrastructure and processes to improve efficiency while optimizing costs. Operational Experimental Not Ready (Pre-AI) ENTERPRISE KNOWLEDGE AI Maturity Spectrum for the Enterprise
  • 10. …with Automated Monitoring and Retraining Entity Extraction Maturity Spectrum Regular Expression Based (RegEx) Auto Classification Custom ML Model …for Entity Extraction …with Active Learning Definition Taxonomy driven categorization of content Definition Traditional supervised learning approach for text classification Considerations Highly dependent on training data Definition Model is re-trained periodically based on human feedback Considerations Increased text classification accuracy Definition Model is automatically re-trained, tested, and deployed Considerations Recommended for large orgs with established DataOps processes Definition Use patterns of characters and operators to match text Considerations Requires explicit definition of rules, and may lead to false positives Considerations Limited to the terms defined in the taxonomy Transformational Institutional Operational Experimental Not Ready (Pre-AI)
  • 11. RegEx Rule: Knowledge Graph University is a Service because [*]University is a Service. This content on EK’s site is rich with knowledge that can be extracted through different approaches. Auto-Classification Rule: ● EKGU is a synonym for Enterprise Knowledge Graph University and this article is about EKGU. ● Information Analyst is a Role, so Information Analyst may take EKGU Custom ML Model ● SPARQL and SHACL are frameworks ● Taxonomy, ontology, and Knowledge Graphs are semantic models ● Graph database is a tool Entity Extraction in Action
  • 12. …with Automated Monitoring and Retraining Source Schema Based Rule Based Custom ML Model …for Relationship Extraction …with Active Learning Definition Custom rule set, borrowing relationships from standard formats Considerations Relies on maintaining rules and may lead to false positives Definition Traditional supervised learning for classifying text between two entities Considerations Highly dependent on training data Relationship Extraction Maturity Spectrum Definition Model is automatically re-trained, tested, and deployed Considerations Recommended for large orgs with established DataOps processes Definition Model is re-trained periodically based on human feedback Considerations Increased text classification accuracy Definition Exploit the schema (JSON, XML, etc.) of the source system Considerations Requires explicit mapping in data source between entities to assign relationships Transformational Institutional Operational Experimental Not Ready (Pre-AI)
  • 13. AI Maturity Spectrum for the Enterprise Revisited Transformational Institutional Operational Experimental Not Ready (Pre-AI) ENTERPRISE KNOWLEDGE Use pattern matching for deterministic entity and relationship extraction Design a starter taxonomy & use it for taxonomy driven graph instantiation Use pre-trained ML models for probabilistic entity and relationship extraction Fine-tune pretrained ML models based on SME feedback to update graph Monitor information extraction quality to automatically retrain ML model
  • 14. Be iterative Start small, then iteratively refine and expand AI integration. Involve your SMEs SMEs can help validate AI performance and provide feedback, ensuring accurate and relevant improvements. Define consumer- facing use cases Use cases should address pain points or challenges faced by your target audience. Invest in quality Invest in establishing robust AI models and structured content and data that align with your use cases. In order to best incorporate AI capabilities into your Knowledge Graph pipeline, there are several key factors to consider: Key Considerations
  • 15. ENTERPRISE KNOWLEDGE Any Questions? Thank you for listening. We are happy to take any questions at this time. Sara Nash snash@enterprise-knowledge.com www.linkedin.com/in/sara-g-nash/ Urmi Majumder umajumder@enterprise-knowledge.com www.linkedin.com/in/urmim/ How prepared is your organization for AI? Take EK’s AI Maturity Assessment: https://s.enterprise-knowledge.com/ekaiassessment