SlideShare a Scribd company logo
A Brief Introduction to
Knowledge Graphs
Taxonomy Boot Camp London
16 October 2019
Presented by
Heather Hedden
Hedden Information Management
▪ Taxonomy consultant
– Independent, through Hedden Information Management
– Previously as an employed and contract consultant
▪ Formerly staff taxonomist
– At various companies: Gale/Cengage Learning, Viziant, First Wind
▪ Instructor of online and onsite taxonomy courses
– Independently through Hedden Information Management
– Previously at Simmons University - Library & Information Science School
▪ Author of The Accidental Taxonomist (2010, 2016, Information Today, Inc.)
About Heather Hedden
2
© 2019 Hedden Information Management
Overview
▪ Knowledge graphs are gaining more interest.
▪ There is some uncertainty in how to definite knowledge graphs.
▪ Knowledge graphs span the fields of knowledge management information
science, information technology, computer science.
▪ There are increasing applications of knowledge graphs.
▪ Knowledge graphs closely relate to ontologies and often include taxonomies.
Introduction to Knowledge Graphs
3© 2019 Hedden Information Management
© 2019 Hedden Information Management 4
© 2019 Hedden Information Management 5
What knowledge graphs are
▪ The organization and representation of a knowledge base as a graph:
as a network of nodes and links, not a table of rows and columns
▪ Usually based on data in graph databases, rather than relational databases
▪ Is both human-readable and machine-readable
▪ Usually includes, but not limited to, visualizations, such as of…
– A display of interconnected nodes and links
– A display of related information in a "fact box“
– An output of graph analytics
Introduction to Knowledge Graphs
6© 2019 Hedden Information Management
Knowledge Graphs
7
Knowledge graph example
https://commons.wikimedia.org/wiki
/File:Wikidata-knowledge-graph-
madame-x-2019.png
Fuzheado [CC BY-SA 4.0
(https://creativecommons.org/licens
es/by-sa/4.0)]
Issues with knowledge graph definitions
▪ “Knowledge graphs” have different meanings from different perspectives:
from knowledge engineers, data engineers, data architects, etc.
▪ Sometimes considered the same as a knowledge base, or at least a
knowledge base that is represented as a graph.
▪ Wikipedia redirects “Knowledge graph” to “Ontology (information science).”
▪ An entire presentation and article on definition issues:
"Towards a Definition of Knowledge Graphs," by Lisa Eherlinger and Wolfram
Wöß, CEUR Workshop Proceedings presentation at SEMANTiCS 2016
http://ceur-ws.org/Vol-1695/paper4.pdf
Defining Knowledge Graphs
8© 2019 Hedden Information Management
Defining Knowledge Graphs
9
Issues with knowledge graph definitions
Related to ontologies, whose definition is also not precise. An ontology can be:
1. A complex knowledge organization system with defined types (classes or
individuals), attribute properties and semantic relations
2. A semantic layer, in accordance with W3C standards, that defines the generic
types, attributes and relations, and can be applied to taxonomies and other
knowledge organization systems
▪ For definition #2 of an ontology, the combination of the generic semantic-
layer ontology along with the specific instances (such as found in a
taxonomy), then is something else, called a knowledge graph.
▪ But the combination may also called an ontology, resembling definition #1,
which results in the conflation of “ontology” and “knowledge graph.”
Defining Knowledge Graphs
10© 2019 Hedden Information Management
Knowledge graphs and ontologies
▪ “A knowledge graph acquires and integrates information into an ontology and
applies a reasoner to derive new knowledge.” - Eherlinger and Wöß, “Towards
a Definition of Knowledge Graphs.”
▪ Whereas an ontology can be a generic model template of how things are
related to each other, a knowledge graph is the actual instance of that model.
▪ A knowledge graph is an ontology + instance data (instance terms and links to
data and content)
➢ Knowledge graphs are ontologies and more.
➢ A knowledge graph may also comprise multiple ontologies, or an ontology and
other vocabularies.
Defining Knowledge Graphs
11© 2019 Hedden Information Management
Creating knowledge graphs
▪ Create or utilize taxonomies, apply ontologies, and link to data/content.
▪ Follow SKOS, OWL, and RDF standards of the W3C.
– For example, all nodes must have URIs (Uniform Resource Identifies).
▪ Graph-database software tools can help.
▪ Data may be added manually or automated/minded, or a combination.
Manual technique is similar to that for creating taxonomies and ontologies,
including:
▪ Inventory of content and data
▪ Development of use cases
▪ Mapping relationships
Introduction to Knowledge Graphs
12© 2019 Hedden Information Management
Knowledge graphs and ontologies both:
▪ Represent nodes (things) and relationships between them
▪ Can be visually represented in the same way of nodes and defined
relationships and then may look the same in the visualization
▪ Are based on Semantic Web standards, such as RDF triples
▪ Tend to have been more the expertise domain of computer scientists and data
scientists of than information professionals/taxonomists, but that’s changing!
Also a growing area of interest in knowledge management (business).
Knowledge Graphs and Ontologies
13© 2019 Hedden Information Management
Knowledge graphs and ontologies are based on RDF
RDF, a standard model for data interchange on the Web, uses URIs to name
things and the relationship between things, which are referred to as triples:
(1) Subject – (2) Predicate – (3) Object.
Subject Predicate Object
SUBJECT PREDICATE
OBJECT
Knowledge Graphs and Ontologies
14
Rome, Italy ItalyCapCityof
CapCity
Rome, ItalyItaly
© 2019 Hedden Information Management
Knowledge graphs and knowledge organization systems
(taxonomies, thesauri, ontologies, etc.)
▪ Knowledge graphs may comprise multiple domains and thus multiple
knowledge organization systems.
▪ Knowledge graphs can link together disparate sources of vocabularies and
data.
Knowledge Graphs and Knowledge Organization Systems
15© 2019 Hedden Information Management
What knowledge graphs can do
▪ Integrate knowledge
▪ Serve data governance
▪ Provide semantic enrichment
▪ Bring structured and unstructured data together
▪ Provide unified view of different kinds of unconnected data sources
▪ Provide a semantic layers on top of the metadata layer
▪ Improve search results beyond machine learning and algorithms
▪ Answer complex user questions instead of merely returning documents on a
topic
▪ Combine with deep text analytics, semantic AI, and machine learning
Uses, Implementations, and Examples
16© 2019 Hedden Information Management
Implementations of knowledge graphs
▪ Recommendation engine (such as in ecommerce)
▪ Expert finder
▪ Question-answering based on data
▪ Enterprise knowledge management
▪ Search and discovery
▪ Customer 360 – view of everything known about customers
▪ Compliance
Implementation usually requires:
▪ a content management system
▪ search engine
Uses, Implementations, and Examples
17© 2019 Hedden Information Management
Examples of implementations
▪ Search engine results
– Google’s Knowledge Graph (since 2012)
Freebase, a proprietary graph database acquired by Google in 2010 when
it bought Metaweb
– Microsoft’s Satori (since 2012)
Microsoft Research’s Trinity graph database and computing platform
▪ Healthdirect Australia - public website health symptom checker
https://www.healthdirect.gov.au/symptom-checker
combining data on initial symptoms, gender and age, and then questions on
proposed additional symptoms
Uses, Implementations, and Examples
18© 2019 Hedden Information Management
Taxonomies in Support of Search
19
Knowledge Graphs from
Google searches
Implementations of knowledge graphs
Companies that have built knowledge graphs
Airbnb
Alibaba
Amazon
Apple
Bank of America
Bloomberg
Facebook
Now smaller, medium-sized companies are also building knowledge
graphs.
Uses, Implementations, and Examples
20
Genentech
Goldman Sachs
JPMorgan Chase
LinkedIn
Microsoft
Uber
Wells Fargo
© 2019 Hedden Information Management
© 2019 Hedden Information Management
Questions/Contact
21
Heather Hedden
Taxonomy Consultant
Hedden Information Management
Carlisle, MA USA
+1 978-467-5195
www.hedden-information.com
accidental-taxonomist.blogspot.com
www.linkedin.com/in/hedden
Twitter: @hhedden

More Related Content

What's hot

A Brief Introduction to SKOS
A Brief Introduction to SKOSA Brief Introduction to SKOS
A Brief Introduction to SKOS
Heather Hedden
 
Taxonomy Design for SharePoint
Taxonomy Design for SharePointTaxonomy Design for SharePoint
Taxonomy Design for SharePoint
Heather Hedden
 
Tools for Taxonomies
Tools for TaxonomiesTools for Taxonomies
Tools for Taxonomies
Earley Information Science
 
Taxonomies, Categories, and Tags in WordPress
Taxonomies, Categories, and Tags in WordPressTaxonomies, Categories, and Tags in WordPress
Taxonomies, Categories, and Tags in WordPress
Heather Hedden
 
Taxonomy Fundamentals Workshop 2013
Taxonomy Fundamentals Workshop 2013Taxonomy Fundamentals Workshop 2013
Taxonomy Fundamentals Workshop 2013
Access Innovations, Inc.
 
Taxonomies for Text Analytics and Auto-indexing
Taxonomies for Text Analytics and Auto-indexingTaxonomies for Text Analytics and Auto-indexing
Taxonomies for Text Analytics and Auto-indexing
Heather Hedden
 
Why Are Taxonomies Necessary?
Why Are Taxonomies Necessary?Why Are Taxonomies Necessary?
Why Are Taxonomies Necessary?
Fred Leise
 
Practice point resource guide feb 2016
Practice point resource guide feb 2016Practice point resource guide feb 2016
Practice point resource guide feb 2016
Jean-Paul de Laureal
 
Synonyms, Alternative Labels, and Nonpreferred Terms
Synonyms, Alternative Labels, and Nonpreferred TermsSynonyms, Alternative Labels, and Nonpreferred Terms
Synonyms, Alternative Labels, and Nonpreferred Terms
Heather Hedden
 
Customer-Focused Thesauri
Customer-Focused ThesauriCustomer-Focused Thesauri
Customer-Focused Thesauri
Heather Hedden
 
Managed metadata in SharePoint 2010
Managed metadata in SharePoint 2010Managed metadata in SharePoint 2010
Managed metadata in SharePoint 2010
Slides2ShareFromPallavi
 

What's hot (11)

A Brief Introduction to SKOS
A Brief Introduction to SKOSA Brief Introduction to SKOS
A Brief Introduction to SKOS
 
Taxonomy Design for SharePoint
Taxonomy Design for SharePointTaxonomy Design for SharePoint
Taxonomy Design for SharePoint
 
Tools for Taxonomies
Tools for TaxonomiesTools for Taxonomies
Tools for Taxonomies
 
Taxonomies, Categories, and Tags in WordPress
Taxonomies, Categories, and Tags in WordPressTaxonomies, Categories, and Tags in WordPress
Taxonomies, Categories, and Tags in WordPress
 
Taxonomy Fundamentals Workshop 2013
Taxonomy Fundamentals Workshop 2013Taxonomy Fundamentals Workshop 2013
Taxonomy Fundamentals Workshop 2013
 
Taxonomies for Text Analytics and Auto-indexing
Taxonomies for Text Analytics and Auto-indexingTaxonomies for Text Analytics and Auto-indexing
Taxonomies for Text Analytics and Auto-indexing
 
Why Are Taxonomies Necessary?
Why Are Taxonomies Necessary?Why Are Taxonomies Necessary?
Why Are Taxonomies Necessary?
 
Practice point resource guide feb 2016
Practice point resource guide feb 2016Practice point resource guide feb 2016
Practice point resource guide feb 2016
 
Synonyms, Alternative Labels, and Nonpreferred Terms
Synonyms, Alternative Labels, and Nonpreferred TermsSynonyms, Alternative Labels, and Nonpreferred Terms
Synonyms, Alternative Labels, and Nonpreferred Terms
 
Customer-Focused Thesauri
Customer-Focused ThesauriCustomer-Focused Thesauri
Customer-Focused Thesauri
 
Managed metadata in SharePoint 2010
Managed metadata in SharePoint 2010Managed metadata in SharePoint 2010
Managed metadata in SharePoint 2010
 

Similar to A Brief Introduction to Knowledge Graphs

Enterprise Knowledge Graphs - Data Summit 2024
Enterprise Knowledge Graphs - Data Summit 2024Enterprise Knowledge Graphs - Data Summit 2024
Enterprise Knowledge Graphs - Data Summit 2024
Enterprise Knowledge
 
Transforming knowledge management for climate action
Transforming knowledge management for climate action  Transforming knowledge management for climate action
Transforming knowledge management for climate action
weADAPT
 
FAIR data: LOUD for all audiences
FAIR data: LOUD for all audiencesFAIR data: LOUD for all audiences
FAIR data: LOUD for all audiences
Alessandro Adamou
 
SKOS as the focal point of linked data strategies
SKOS as the focal point of linked data strategiesSKOS as the focal point of linked data strategies
SKOS as the focal point of linked data strategies
Semantic Web Company
 
Serge Ravet - Reinventing the e-Portfolio
Serge Ravet - Reinventing the e-PortfolioSerge Ravet - Reinventing the e-Portfolio
Serge Ravet - Reinventing the e-Portfolio
Bestr
 
Reinventing the ePortfolio with Open Badges
Reinventing the ePortfolio with Open BadgesReinventing the ePortfolio with Open Badges
Reinventing the ePortfolio with Open Badges
Serge Ravet
 
Open Badge Passport:
Open Badge Passport: Open Badge Passport:
Open Badge Passport:
Serge Ravet
 
Enterprise linked data - open or closed, Andreas Blumauer, Keynote SMWCon 2014
Enterprise linked data - open or closed, Andreas Blumauer, Keynote SMWCon 2014Enterprise linked data - open or closed, Andreas Blumauer, Keynote SMWCon 2014
Enterprise linked data - open or closed, Andreas Blumauer, Keynote SMWCon 2014
KDZ - Zentrum für Verwaltungsforschung
 
PoolParty Semantic Classifier
PoolParty Semantic ClassifierPoolParty Semantic Classifier
PoolParty Semantic Classifier
Semantic Web Company
 
CC TEL- Simulation-based co-design of algorithms
CC TEL- Simulation-based co-design of algorithmsCC TEL- Simulation-based co-design of algorithms
CC TEL- Simulation-based co-design of algorithms
Sebastian Dennerlein
 
Estrategies data
Estrategies dataEstrategies data
Estrategies data
Lee Schlenker
 
Understanding Information Architecture
Understanding Information ArchitectureUnderstanding Information Architecture
Understanding Information Architecture
Scott Abel
 
Data Storytelling
Data StorytellingData Storytelling
Data Storytelling
Lee Schlenker
 
Learning 360: Crafting a Comprehensive View of Learning by Using a Graph
Learning 360: Crafting a Comprehensive View of Learning by Using a GraphLearning 360: Crafting a Comprehensive View of Learning by Using a Graph
Learning 360: Crafting a Comprehensive View of Learning by Using a Graph
Enterprise Knowledge
 
Introduction to Knowledge Graphs for Information Architects.pdf
Introduction to Knowledge Graphs for Information Architects.pdfIntroduction to Knowledge Graphs for Information Architects.pdf
Introduction to Knowledge Graphs for Information Architects.pdf
Heather Hedden
 
GTN-Québec_2010 05-25
GTN-Québec_2010 05-25GTN-Québec_2010 05-25
GTN-Québec_2010 05-25
Simon Grant
 
Adaptive Business Capability
Adaptive Business CapabilityAdaptive Business Capability
Adaptive Business Capability
Malcolm Ryder
 
Principles of data visualisation 2020
Principles of data visualisation 2020Principles of data visualisation 2020
Principles of data visualisation 2020
Marié Roux
 
Structured writing presentation to London Content Strategy Meetup
Structured writing presentation to London Content Strategy MeetupStructured writing presentation to London Content Strategy Meetup
Structured writing presentation to London Content Strategy Meetup
Ellis Pratt
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
Enterprise Knowledge
 

Similar to A Brief Introduction to Knowledge Graphs (20)

Enterprise Knowledge Graphs - Data Summit 2024
Enterprise Knowledge Graphs - Data Summit 2024Enterprise Knowledge Graphs - Data Summit 2024
Enterprise Knowledge Graphs - Data Summit 2024
 
Transforming knowledge management for climate action
Transforming knowledge management for climate action  Transforming knowledge management for climate action
Transforming knowledge management for climate action
 
FAIR data: LOUD for all audiences
FAIR data: LOUD for all audiencesFAIR data: LOUD for all audiences
FAIR data: LOUD for all audiences
 
SKOS as the focal point of linked data strategies
SKOS as the focal point of linked data strategiesSKOS as the focal point of linked data strategies
SKOS as the focal point of linked data strategies
 
Serge Ravet - Reinventing the e-Portfolio
Serge Ravet - Reinventing the e-PortfolioSerge Ravet - Reinventing the e-Portfolio
Serge Ravet - Reinventing the e-Portfolio
 
Reinventing the ePortfolio with Open Badges
Reinventing the ePortfolio with Open BadgesReinventing the ePortfolio with Open Badges
Reinventing the ePortfolio with Open Badges
 
Open Badge Passport:
Open Badge Passport: Open Badge Passport:
Open Badge Passport:
 
Enterprise linked data - open or closed, Andreas Blumauer, Keynote SMWCon 2014
Enterprise linked data - open or closed, Andreas Blumauer, Keynote SMWCon 2014Enterprise linked data - open or closed, Andreas Blumauer, Keynote SMWCon 2014
Enterprise linked data - open or closed, Andreas Blumauer, Keynote SMWCon 2014
 
PoolParty Semantic Classifier
PoolParty Semantic ClassifierPoolParty Semantic Classifier
PoolParty Semantic Classifier
 
CC TEL- Simulation-based co-design of algorithms
CC TEL- Simulation-based co-design of algorithmsCC TEL- Simulation-based co-design of algorithms
CC TEL- Simulation-based co-design of algorithms
 
Estrategies data
Estrategies dataEstrategies data
Estrategies data
 
Understanding Information Architecture
Understanding Information ArchitectureUnderstanding Information Architecture
Understanding Information Architecture
 
Data Storytelling
Data StorytellingData Storytelling
Data Storytelling
 
Learning 360: Crafting a Comprehensive View of Learning by Using a Graph
Learning 360: Crafting a Comprehensive View of Learning by Using a GraphLearning 360: Crafting a Comprehensive View of Learning by Using a Graph
Learning 360: Crafting a Comprehensive View of Learning by Using a Graph
 
Introduction to Knowledge Graphs for Information Architects.pdf
Introduction to Knowledge Graphs for Information Architects.pdfIntroduction to Knowledge Graphs for Information Architects.pdf
Introduction to Knowledge Graphs for Information Architects.pdf
 
GTN-Québec_2010 05-25
GTN-Québec_2010 05-25GTN-Québec_2010 05-25
GTN-Québec_2010 05-25
 
Adaptive Business Capability
Adaptive Business CapabilityAdaptive Business Capability
Adaptive Business Capability
 
Principles of data visualisation 2020
Principles of data visualisation 2020Principles of data visualisation 2020
Principles of data visualisation 2020
 
Structured writing presentation to London Content Strategy Meetup
Structured writing presentation to London Content Strategy MeetupStructured writing presentation to London Content Strategy Meetup
Structured writing presentation to London Content Strategy Meetup
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 

More from Heather Hedden

Thesauri for Indexing Support / Thesauri zur Unterstützung der Registererstel...
Thesauri for Indexing Support / Thesauri zur Unterstützung der Registererstel...Thesauri for Indexing Support / Thesauri zur Unterstützung der Registererstel...
Thesauri for Indexing Support / Thesauri zur Unterstützung der Registererstel...
Heather Hedden
 
Managing Mature Taxonomies: Resolving Orphan Terms
Managing Mature Taxonomies: Resolving Orphan TermsManaging Mature Taxonomies: Resolving Orphan Terms
Managing Mature Taxonomies: Resolving Orphan Terms
Heather Hedden
 
Taxonomy Displays: Bridging UX & Taxonomy Design
Taxonomy Displays: Bridging UX & Taxonomy DesignTaxonomy Displays: Bridging UX & Taxonomy Design
Taxonomy Displays: Bridging UX & Taxonomy Design
Heather Hedden
 
Testing Taxonomies
Testing TaxonomiesTesting Taxonomies
Testing Taxonomies
Heather Hedden
 
Taxonomies for E-commerce
Taxonomies for E-commerceTaxonomies for E-commerce
Taxonomies for E-commerce
Heather Hedden
 
Mapping, Merging, and Multilingual Taxonomies
Mapping, Merging, and Multilingual TaxonomiesMapping, Merging, and Multilingual Taxonomies
Mapping, Merging, and Multilingual Taxonomies
Heather Hedden
 
Making Decisions in Creating Taxonomies
Making Decisions in Creating TaxonomiesMaking Decisions in Creating Taxonomies
Making Decisions in Creating Taxonomies
Heather Hedden
 
Taxonomies for Human vs Auto-Indexing
Taxonomies for Human vs Auto-IndexingTaxonomies for Human vs Auto-Indexing
Taxonomies for Human vs Auto-Indexing
Heather Hedden
 

More from Heather Hedden (8)

Thesauri for Indexing Support / Thesauri zur Unterstützung der Registererstel...
Thesauri for Indexing Support / Thesauri zur Unterstützung der Registererstel...Thesauri for Indexing Support / Thesauri zur Unterstützung der Registererstel...
Thesauri for Indexing Support / Thesauri zur Unterstützung der Registererstel...
 
Managing Mature Taxonomies: Resolving Orphan Terms
Managing Mature Taxonomies: Resolving Orphan TermsManaging Mature Taxonomies: Resolving Orphan Terms
Managing Mature Taxonomies: Resolving Orphan Terms
 
Taxonomy Displays: Bridging UX & Taxonomy Design
Taxonomy Displays: Bridging UX & Taxonomy DesignTaxonomy Displays: Bridging UX & Taxonomy Design
Taxonomy Displays: Bridging UX & Taxonomy Design
 
Testing Taxonomies
Testing TaxonomiesTesting Taxonomies
Testing Taxonomies
 
Taxonomies for E-commerce
Taxonomies for E-commerceTaxonomies for E-commerce
Taxonomies for E-commerce
 
Mapping, Merging, and Multilingual Taxonomies
Mapping, Merging, and Multilingual TaxonomiesMapping, Merging, and Multilingual Taxonomies
Mapping, Merging, and Multilingual Taxonomies
 
Making Decisions in Creating Taxonomies
Making Decisions in Creating TaxonomiesMaking Decisions in Creating Taxonomies
Making Decisions in Creating Taxonomies
 
Taxonomies for Human vs Auto-Indexing
Taxonomies for Human vs Auto-IndexingTaxonomies for Human vs Auto-Indexing
Taxonomies for Human vs Auto-Indexing
 

Recently uploaded

High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...
High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...
High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...
bhumivarma35300
 
Types of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technologyTypes of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technology
ldtexsolbl
 
How Social Media Hackers Help You to See Your Wife's Message.pdf
How Social Media Hackers Help You to See Your Wife's Message.pdfHow Social Media Hackers Help You to See Your Wife's Message.pdf
How Social Media Hackers Help You to See Your Wife's Message.pdf
HackersList
 
The importance of Quality Assurance for ICT Standardization
The importance of Quality Assurance for ICT StandardizationThe importance of Quality Assurance for ICT Standardization
The importance of Quality Assurance for ICT Standardization
Axel Rennoch
 
WhatsApp Spy Online Trackers and Monitoring Apps
WhatsApp Spy Online Trackers and Monitoring AppsWhatsApp Spy Online Trackers and Monitoring Apps
WhatsApp Spy Online Trackers and Monitoring Apps
HackersList
 
Salesforce AI & Einstein Copilot Workshop
Salesforce AI & Einstein Copilot WorkshopSalesforce AI & Einstein Copilot Workshop
Salesforce AI & Einstein Copilot Workshop
CEPTES Software Inc
 
Vulnerability Management: A Comprehensive Overview
Vulnerability Management: A Comprehensive OverviewVulnerability Management: A Comprehensive Overview
Vulnerability Management: A Comprehensive Overview
Steven Carlson
 
Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...
Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...
Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...
shanihomely
 
July Patch Tuesday
July Patch TuesdayJuly Patch Tuesday
July Patch Tuesday
Ivanti
 
[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf
[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf
[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf
Kief Morris
 
Data Integration Basics: Merging & Joining Data
Data Integration Basics: Merging & Joining DataData Integration Basics: Merging & Joining Data
Data Integration Basics: Merging & Joining Data
Safe Software
 
CHAPTER-8 COMPONENTS OF COMPUTER SYSTEM CLASS 9 CBSE
CHAPTER-8 COMPONENTS OF COMPUTER SYSTEM CLASS 9 CBSECHAPTER-8 COMPONENTS OF COMPUTER SYSTEM CLASS 9 CBSE
CHAPTER-8 COMPONENTS OF COMPUTER SYSTEM CLASS 9 CBSE
kumarjarun2010
 
Active Inference is a veryyyyyyyyyyyyyyyyyyyyyyyy
Active Inference is a veryyyyyyyyyyyyyyyyyyyyyyyyActive Inference is a veryyyyyyyyyyyyyyyyyyyyyyyy
Active Inference is a veryyyyyyyyyyyyyyyyyyyyyyyy
RaminGhanbari2
 
(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...
(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...
(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...
Priyanka Aash
 
Best Practices for Effectively Running dbt in Airflow.pdf
Best Practices for Effectively Running dbt in Airflow.pdfBest Practices for Effectively Running dbt in Airflow.pdf
Best Practices for Effectively Running dbt in Airflow.pdf
Tatiana Al-Chueyr
 
TrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-In
TrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-InTrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-In
TrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-In
TrustArc
 
Opencast Summit 2024 — Opencast @ University of Münster
Opencast Summit 2024 — Opencast @ University of MünsterOpencast Summit 2024 — Opencast @ University of Münster
Opencast Summit 2024 — Opencast @ University of Münster
Matthias Neugebauer
 
Figma AI Design Generator_ In-Depth Review.pdf
Figma AI Design Generator_ In-Depth Review.pdfFigma AI Design Generator_ In-Depth Review.pdf
Figma AI Design Generator_ In-Depth Review.pdf
Management Institute of Skills Development
 
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
sunilverma7884
 
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and DisadvantagesBLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
SAI KAILASH R
 

Recently uploaded (20)

High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...
High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...
High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...
 
Types of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technologyTypes of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technology
 
How Social Media Hackers Help You to See Your Wife's Message.pdf
How Social Media Hackers Help You to See Your Wife's Message.pdfHow Social Media Hackers Help You to See Your Wife's Message.pdf
How Social Media Hackers Help You to See Your Wife's Message.pdf
 
The importance of Quality Assurance for ICT Standardization
The importance of Quality Assurance for ICT StandardizationThe importance of Quality Assurance for ICT Standardization
The importance of Quality Assurance for ICT Standardization
 
WhatsApp Spy Online Trackers and Monitoring Apps
WhatsApp Spy Online Trackers and Monitoring AppsWhatsApp Spy Online Trackers and Monitoring Apps
WhatsApp Spy Online Trackers and Monitoring Apps
 
Salesforce AI & Einstein Copilot Workshop
Salesforce AI & Einstein Copilot WorkshopSalesforce AI & Einstein Copilot Workshop
Salesforce AI & Einstein Copilot Workshop
 
Vulnerability Management: A Comprehensive Overview
Vulnerability Management: A Comprehensive OverviewVulnerability Management: A Comprehensive Overview
Vulnerability Management: A Comprehensive Overview
 
Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...
Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...
Premium Girls Call Mumbai 9920725232 Unlimited Short Providing Girls Service ...
 
July Patch Tuesday
July Patch TuesdayJuly Patch Tuesday
July Patch Tuesday
 
[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf
[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf
[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf
 
Data Integration Basics: Merging & Joining Data
Data Integration Basics: Merging & Joining DataData Integration Basics: Merging & Joining Data
Data Integration Basics: Merging & Joining Data
 
CHAPTER-8 COMPONENTS OF COMPUTER SYSTEM CLASS 9 CBSE
CHAPTER-8 COMPONENTS OF COMPUTER SYSTEM CLASS 9 CBSECHAPTER-8 COMPONENTS OF COMPUTER SYSTEM CLASS 9 CBSE
CHAPTER-8 COMPONENTS OF COMPUTER SYSTEM CLASS 9 CBSE
 
Active Inference is a veryyyyyyyyyyyyyyyyyyyyyyyy
Active Inference is a veryyyyyyyyyyyyyyyyyyyyyyyyActive Inference is a veryyyyyyyyyyyyyyyyyyyyyyyy
Active Inference is a veryyyyyyyyyyyyyyyyyyyyyyyy
 
(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...
(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...
(CISOPlatform Summit & SACON 2024) Keynote _ Power Digital Identities With AI...
 
Best Practices for Effectively Running dbt in Airflow.pdf
Best Practices for Effectively Running dbt in Airflow.pdfBest Practices for Effectively Running dbt in Airflow.pdf
Best Practices for Effectively Running dbt in Airflow.pdf
 
TrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-In
TrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-InTrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-In
TrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-In
 
Opencast Summit 2024 — Opencast @ University of Münster
Opencast Summit 2024 — Opencast @ University of MünsterOpencast Summit 2024 — Opencast @ University of Münster
Opencast Summit 2024 — Opencast @ University of Münster
 
Figma AI Design Generator_ In-Depth Review.pdf
Figma AI Design Generator_ In-Depth Review.pdfFigma AI Design Generator_ In-Depth Review.pdf
Figma AI Design Generator_ In-Depth Review.pdf
 
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
 
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and DisadvantagesBLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
 

A Brief Introduction to Knowledge Graphs

  • 1. A Brief Introduction to Knowledge Graphs Taxonomy Boot Camp London 16 October 2019 Presented by Heather Hedden Hedden Information Management
  • 2. ▪ Taxonomy consultant – Independent, through Hedden Information Management – Previously as an employed and contract consultant ▪ Formerly staff taxonomist – At various companies: Gale/Cengage Learning, Viziant, First Wind ▪ Instructor of online and onsite taxonomy courses – Independently through Hedden Information Management – Previously at Simmons University - Library & Information Science School ▪ Author of The Accidental Taxonomist (2010, 2016, Information Today, Inc.) About Heather Hedden 2 © 2019 Hedden Information Management
  • 3. Overview ▪ Knowledge graphs are gaining more interest. ▪ There is some uncertainty in how to definite knowledge graphs. ▪ Knowledge graphs span the fields of knowledge management information science, information technology, computer science. ▪ There are increasing applications of knowledge graphs. ▪ Knowledge graphs closely relate to ontologies and often include taxonomies. Introduction to Knowledge Graphs 3© 2019 Hedden Information Management
  • 4. © 2019 Hedden Information Management 4
  • 5. © 2019 Hedden Information Management 5
  • 6. What knowledge graphs are ▪ The organization and representation of a knowledge base as a graph: as a network of nodes and links, not a table of rows and columns ▪ Usually based on data in graph databases, rather than relational databases ▪ Is both human-readable and machine-readable ▪ Usually includes, but not limited to, visualizations, such as of… – A display of interconnected nodes and links – A display of related information in a "fact box“ – An output of graph analytics Introduction to Knowledge Graphs 6© 2019 Hedden Information Management
  • 7. Knowledge Graphs 7 Knowledge graph example https://commons.wikimedia.org/wiki /File:Wikidata-knowledge-graph- madame-x-2019.png Fuzheado [CC BY-SA 4.0 (https://creativecommons.org/licens es/by-sa/4.0)]
  • 8. Issues with knowledge graph definitions ▪ “Knowledge graphs” have different meanings from different perspectives: from knowledge engineers, data engineers, data architects, etc. ▪ Sometimes considered the same as a knowledge base, or at least a knowledge base that is represented as a graph. ▪ Wikipedia redirects “Knowledge graph” to “Ontology (information science).” ▪ An entire presentation and article on definition issues: "Towards a Definition of Knowledge Graphs," by Lisa Eherlinger and Wolfram Wöß, CEUR Workshop Proceedings presentation at SEMANTiCS 2016 http://ceur-ws.org/Vol-1695/paper4.pdf Defining Knowledge Graphs 8© 2019 Hedden Information Management
  • 10. Issues with knowledge graph definitions Related to ontologies, whose definition is also not precise. An ontology can be: 1. A complex knowledge organization system with defined types (classes or individuals), attribute properties and semantic relations 2. A semantic layer, in accordance with W3C standards, that defines the generic types, attributes and relations, and can be applied to taxonomies and other knowledge organization systems ▪ For definition #2 of an ontology, the combination of the generic semantic- layer ontology along with the specific instances (such as found in a taxonomy), then is something else, called a knowledge graph. ▪ But the combination may also called an ontology, resembling definition #1, which results in the conflation of “ontology” and “knowledge graph.” Defining Knowledge Graphs 10© 2019 Hedden Information Management
  • 11. Knowledge graphs and ontologies ▪ “A knowledge graph acquires and integrates information into an ontology and applies a reasoner to derive new knowledge.” - Eherlinger and Wöß, “Towards a Definition of Knowledge Graphs.” ▪ Whereas an ontology can be a generic model template of how things are related to each other, a knowledge graph is the actual instance of that model. ▪ A knowledge graph is an ontology + instance data (instance terms and links to data and content) ➢ Knowledge graphs are ontologies and more. ➢ A knowledge graph may also comprise multiple ontologies, or an ontology and other vocabularies. Defining Knowledge Graphs 11© 2019 Hedden Information Management
  • 12. Creating knowledge graphs ▪ Create or utilize taxonomies, apply ontologies, and link to data/content. ▪ Follow SKOS, OWL, and RDF standards of the W3C. – For example, all nodes must have URIs (Uniform Resource Identifies). ▪ Graph-database software tools can help. ▪ Data may be added manually or automated/minded, or a combination. Manual technique is similar to that for creating taxonomies and ontologies, including: ▪ Inventory of content and data ▪ Development of use cases ▪ Mapping relationships Introduction to Knowledge Graphs 12© 2019 Hedden Information Management
  • 13. Knowledge graphs and ontologies both: ▪ Represent nodes (things) and relationships between them ▪ Can be visually represented in the same way of nodes and defined relationships and then may look the same in the visualization ▪ Are based on Semantic Web standards, such as RDF triples ▪ Tend to have been more the expertise domain of computer scientists and data scientists of than information professionals/taxonomists, but that’s changing! Also a growing area of interest in knowledge management (business). Knowledge Graphs and Ontologies 13© 2019 Hedden Information Management
  • 14. Knowledge graphs and ontologies are based on RDF RDF, a standard model for data interchange on the Web, uses URIs to name things and the relationship between things, which are referred to as triples: (1) Subject – (2) Predicate – (3) Object. Subject Predicate Object SUBJECT PREDICATE OBJECT Knowledge Graphs and Ontologies 14 Rome, Italy ItalyCapCityof CapCity Rome, ItalyItaly © 2019 Hedden Information Management
  • 15. Knowledge graphs and knowledge organization systems (taxonomies, thesauri, ontologies, etc.) ▪ Knowledge graphs may comprise multiple domains and thus multiple knowledge organization systems. ▪ Knowledge graphs can link together disparate sources of vocabularies and data. Knowledge Graphs and Knowledge Organization Systems 15© 2019 Hedden Information Management
  • 16. What knowledge graphs can do ▪ Integrate knowledge ▪ Serve data governance ▪ Provide semantic enrichment ▪ Bring structured and unstructured data together ▪ Provide unified view of different kinds of unconnected data sources ▪ Provide a semantic layers on top of the metadata layer ▪ Improve search results beyond machine learning and algorithms ▪ Answer complex user questions instead of merely returning documents on a topic ▪ Combine with deep text analytics, semantic AI, and machine learning Uses, Implementations, and Examples 16© 2019 Hedden Information Management
  • 17. Implementations of knowledge graphs ▪ Recommendation engine (such as in ecommerce) ▪ Expert finder ▪ Question-answering based on data ▪ Enterprise knowledge management ▪ Search and discovery ▪ Customer 360 – view of everything known about customers ▪ Compliance Implementation usually requires: ▪ a content management system ▪ search engine Uses, Implementations, and Examples 17© 2019 Hedden Information Management
  • 18. Examples of implementations ▪ Search engine results – Google’s Knowledge Graph (since 2012) Freebase, a proprietary graph database acquired by Google in 2010 when it bought Metaweb – Microsoft’s Satori (since 2012) Microsoft Research’s Trinity graph database and computing platform ▪ Healthdirect Australia - public website health symptom checker https://www.healthdirect.gov.au/symptom-checker combining data on initial symptoms, gender and age, and then questions on proposed additional symptoms Uses, Implementations, and Examples 18© 2019 Hedden Information Management
  • 19. Taxonomies in Support of Search 19 Knowledge Graphs from Google searches
  • 20. Implementations of knowledge graphs Companies that have built knowledge graphs Airbnb Alibaba Amazon Apple Bank of America Bloomberg Facebook Now smaller, medium-sized companies are also building knowledge graphs. Uses, Implementations, and Examples 20 Genentech Goldman Sachs JPMorgan Chase LinkedIn Microsoft Uber Wells Fargo © 2019 Hedden Information Management
  • 21. © 2019 Hedden Information Management Questions/Contact 21 Heather Hedden Taxonomy Consultant Hedden Information Management Carlisle, MA USA +1 978-467-5195 www.hedden-information.com accidental-taxonomist.blogspot.com www.linkedin.com/in/hedden Twitter: @hhedden