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Ann Bernath, Software Systems Engineer, JPL
Bess Schrader, Senior Consultant,
Enterprise Knowledge
Approved for Public Release: JPL CL#22-5900
JPL’s Institutional Knowledge Graph II
A Foundation for Constructing
Enterprise and Domain-Specific
Semantic Data Sets
Who are we?
2
11/9/2022 Approved for Public Release: JPL CL#22-5900
JPL: A NASA FFRDC Owned and Managed by Caltech
Approved for Public Release: JPL CL#22-5900 3
11/9/2022
Ann Bernath, Jet Propulsion Laboratory
1978 2015 2022
Taxonomies
Ontologies
Software
Systems
Engineering
Document
Management
Word
Processing
Information
Modeling
Approved for Public Release: JPL CL#22-5900 4
11/9/2022
Bess Schrader, Enterprise Knowledge
employed by
Information Science
degree
had internship
focus
focus
taxonomy
Linked data
blew mind
of
Approved for Public Release: JPL CL#22-5900 5
11/9/2022
Last Year at (virtual) KMWorld
Approved for Public Release: JPL CL#22-5900 6
11/9/2022
Leveraging semantic technologies to add context to structured and unstructured data
JPL’s Institutional
Knowledge Graph
Person info
Institutional data
§ OCO 2
§ OCO2
§ OCO-2
§ Orbiting Carbon Observatory-2
§ Orbiting Carbon Observatory 2
We talked about our problem: multiple systems that maintain their
own copies of enterprise data often resulting in out-of-sync data
Approved for Public Release: JPL CL#22-5900 7
11/9/2022
Institutional info & expertise was hard to find
Approved for Public Release: JPL CL#22-5900
Who has a background in chemistry?
Where’s the Robotics Lab?
Who do I call if there’s a hazard?
Who has experience on recent
Mars lander missions? …
8
11/9/2022
Desired state
MSL
Jane Brown
Mars 2020
John Brown
Mars Sample Return
Red Green
Who worked on recent Mars lander
missions?
Topics
q Projects
q Mars 2020
q Mission Targets
q Mars
q Organizations
q Engineering and Science
q Applications
q Issue Tracking
q Publications
q Science
q People
…
Institutional
Knowledge
Graph
“One-stop shopping” Answers to questions
Institutional Context
Approved for Public Release: JPL CL#22-5900 9
11/9/2022
JPL Domains
(Business, Eng, Science)
Institutional Layer
(Organizations, People, Projects,
Facilities, …)
Institutional Model/Ontology
W3C Syntax and Protocol Standards
(OWL, RDF, RDFS, SHACL, SKOS, SPARQL, …)
We realized we needed to build an institutional layer in
our semantic pyramid
Institutional
layer was
missing
Approved for Public Release: JPL CL#22-5900 10
11/9/2022
Our Solution:
The Institutional
Knowledge
Graph
Approved for Public Release: JPL CL#22-5900
M
ission
Targets
Projects
Locations
P
eo
pl
e
11
11/9/2022
Data Domains
People
•Position
•Education
•Role…
Organizations
•Hierarchy
•Managers
•Membership
(Physical)
Locations
•Facilities
•Buildings
•Labs
Work
Projects
Key Roles
Milestones
Digital
Locations
(Apps,
Repositories)
Documents
and
Publications
Approved for Public Release: JPL CL#22-5900 12
11/9/2022
IKG History Graph
Our Expanding Semantic-Verse
13
11/9/2022 Approved for Public Release: JPL CL#22-5900
JPL Domains
(Business, Eng, Science)
Institutional Knowledge Graph
(Organizations, People, Projects,
Facilities, …)
Institutional Model/Ontology
(Unique identifiers for institutional concepts)
W3C Syntax and Protocol Standards
(OWL, RDF, RDFS, SHACL, SKOS, SPARQL, …)
Our semantic pyramid takes shape
Domain specific
knowledge graphs
can build upon it
Approved for Public Release: JPL CL#22-5900 14
11/9/2022
Now that we’ve
laid this
foundation
Domain data sets are beginning to
leverage our semantic foundation,
enriching IKG in return
11/9/2022 Approved for Public Release: JPL CL#22-5900 15
Institutional
Knowledge
Graph
Labs
• Responsibilities
• Facilities
Master List of
Controlled
Records
• Retention Schedules
• Roles
Business Event
Transactions
• Work Activity
• Experience
Infrastructure
Technical
Architecture
Description
• Functions
• Systems
• Services
• Roles
JPL Taxonomy
• Real-World Concepts
Science
Taxonomy
• Projects
• Expertise
For instance, the Labs data set enriches what the IKG
knows about people, organizations, and locations
11/9/2022 Approved for Public Release: JPL CL#22-5900 16
Robotics
Lab
Jane Doe
John
Smith
Robotics
Design
Organization
Building 555
Room 000
Building 555
Room 000B
responsible for
accountable for
managed by
located in
located in
The IKG enriches
lifecycle event
data while
lifecycle event
data enriches
what the IKG
knows about a
person’s
expertise
11/9/2022 Approved for Public Release: JPL CL#22-5900 17
Knowledge Graph Symbiosis
18
11/9/2022 Approved for Public Release: JPL CL#22-5900
Techniques to Federate or Synchronize
Semantic Data Sets
Why bother connecting data
sets?
11/9/2022 Approved for Public Release: JPL CL#22-5900 19
Connecting data from multiple systems is important
not only does it
allow us to connect
the dots …
11/9/2022 Approved for Public Release: JPL CL#22-5900 20
Connecting data from multiple systems is important
…it also allows us to find discrepancies between different systems.
For example, the IKG has found:
Buildings appearing in one location application but not
another
People incorrectly listed as organization managers in
HR system
People that are no longer at JPL assigned to active
roles
11/9/2022 Approved for Public Release: JPL CL#22-5900 21
The technical nitty-gritty
11/9/2022 Approved for Public Release: JPL CL#22-5900 22
Show of hands:
who knows what a URI is?
11/9/2022 Approved for Public Release: JPL CL#22-5900 23
a lot of us only a few of us
How do we encode this in a way that machines can understand it?
Approved for Public Release: JPL CL#22-5900 24
11/9/2022
Let’s Pause for Some RDF Basics
Resource Description Framework
RDF – Resource Description
Framework
● “Things not strings”
● W3C standard
● Model for data interchange on the web
● Allows integration of differing schemas or
representations of data
Approved for Public Release: JPL CL#22-5900 25
11/9/2022
Robotics
Lab
John
Smith
accountable for
Uniform Resource Identifiers (URI)
To be machine readable, all of
our bubbles and lines (i.e. the
elements of our triple) need a
Uniform Resource Identifier
(URI). URIs are also known as
IRIs (Internationalized
Resource Identifier)
URIs are unique identifiers that
look like URLs (although they
don’t actually have to go
anywhere)
Approved for Public Release: JPL CL#22-5900 26
11/9/2022
<http://example.jpl.nasa.gov/ontologies/ikg/Lab/Robotics_Lab>
<http://example.jpl.nasa.gov/ontologies/ikg/Person/jsmith>
<http://example.jpl.nasa.gov/ontologies/ikg#accountableFor>
Uniform Resource Identifiers (URI)
Using URIs, our Robotics Lab example would look like this:
Approved for Public Release: JPL CL#22-5900 27
11/9/2022
Prefixes (aka Namespaces)
<http://example.jpl.nasa.gov/ontologies/ikg/Lab/Robotics_Lab>
lab
@prefix lab: <http://example.jpl.nasa.gov/ontologies/ikg/Lab/>
lab:Robotics_Lab
<http://example.jpl.nasa.gov/ontologies/ikg/Lab/Robotics_Lab>
Approved for Public Release: JPL CL#22-5900 28
11/9/2022
lab:Robotics_Lab
person:jsmith
ikg:accountableFor
Uniform Resource Identifiers (URI)
Using prefixes,
our Robotics Lab
example would
look like this:
Approved for Public Release: JPL CL#22-5900 29
11/9/2022
@prefix ikg: <http://example.jpl.nasa.gov/ontologies/ikg#>
@prefix lab: <http://example.jpl.nasa.gov/ontologies/ikg/Lab/>
@prefix person: <http://example.jpl.nasa.gov/ontologies/ikg/Person/>
Namespaces
In addition to making URIs easier for humans to read,
namespaces they can also help with establishing data ownership
and governance. For example:
ikg:accountableFor
The “ikg” prefix indicates this relationship is owned by the Institutional
Knowledge Graph (IKG). It may have a specific meaning in that context, and
changes are controlled by the IKG team.
hr:accountableFor
The “hr” prefix indicates this relationship is owned by Human Resources. It
may have a specific meaning in that context, and changes are controlled by
the HR team.
Approved for Public Release: JPL CL#22-5900 30
11/9/2022
Uniform Resource Identifiers (URI)
URIs are critical to building knowledge graphs, especially for ensuring that
different semantic data sets can talk to each other.
Reusing URIs across datasets that refer to the same concept helps ensure
that:
Approved for Public Release: JPL CL#22-5900 31
11/9/2022
entities only need to be defined once
entities have a clear owner
semantic data sets can be linked
How to link data sets
11/9/2022 Approved for Public Release: JPL CL#22-5900 32
Linking Methods
11/9/2022 Approved for Public Release: JPL CL#22-5900 33
• Re-use URIs for institutional entities
enabling federated queries
Ideal
• Match on “hooks” (important properties)
such as key identifiers (employee
numbers, usernames, …)
Pretty good
• Alternative labels/educated guesses
(matching rules)
Probable
• Manual review
Sometimes
required
How do we match or link entities across different semantic
data sets?
Linking Methods – Reusing URIs
11/9/2022 Approved for Public Release: JPL CL#22-5900 34
• Use URIs for institutional entities
enabling federated queries
Ideal
• Hooks such as key identifiers
(employee numbers, usernames, …)
Pretty good
• Alternative labels/educated guesses
(matching rules)
Probable
• Manual review
Sometimes
required
Data Set 1
person:bschrader a ikg:Person ;
rdfs:label “Bess P Schrader” .
Data Set 2
doc:1234 a jpl:Document ;
rdfs:label “KM World 2022 Presentation” ;
jpl:createdBy person:bschrader .
In the best case scenario, owners/creators of semantic data sets reuse
URIs between data sets at the time of creation, so there’s no guess
work involved in matching entities across data sets.
Linking Methods – Using Hooks
11/9/2022 Approved for Public Release: JPL CL#22-5900 35
Data Set 1
person:bschrader a ikg:Person ;
rdfs:label “Bess P Schrader” ;
ikg:username “bschrader” .
Data Set 2
doc:1234 a jpl:Document ;
rdfs:label “KM World 2022 Presentation” ;
jpl:createdBy jpl:Person_123456 .
jpl:Person_123456 jpl:username “bschrader” .
• Hooks such as key identifiers
(employee numbers, usernames,
…)
Pretty good
If the same URIs aren’t used across data sets, commonly used
institutional identifiers (like usernames, department codes, etc.) can be
another good option for finding entity matches.
Linking Methods – Matching Rules
11/9/2022 Approved for Public Release: JPL CL#22-5900 36
Data Set 1
person:bschrader a ikg:Person ;
rdfs:label “Bess P Schrader” ;
ikg:username “bschrader” ;
ikg:firstName “Bess” ;
ikg:lastName “Schrader” ;
ikg:memberOf org:1234 .
org:1234 a ikg:Organization ;
ikg:organizationCode “1234” .
Data Set 2
doc:1234 a jpl:Document ;
rdfs:label “KM World 2022 Presentation” ;
jpl:createdBy jpl:Person_123456 ;
jpl:organization “1234” .
jpl:Person_123456 rdfs:label “B. Schrader” .
• Alternative labels/educated
guesses (matching rules)
Probable
Lacking re-used URIs or institutional identifiers, we often have to make
up our own matching logic to determine if two entities are the same.
Linking Methods – Matching Rules
11/9/2022 Approved for Public Release: JPL CL#22-5900 37
Data Set 1
person:bschrader a ikg:Person ;
rdfs:label “Bess P Schrader” ;
ikg:username “bschrader” ;
ikg:firstName “Bess” ;
ikg:lastName “Schrader” ;
ikg:memberOf org:1234 .
org:1234 a ikg:Organization ;
ikg:organizationCode “1234” .
Data Set 2
doc:1234 a jpl:Document ;
rdfs:label “KM World 2022 Presentation” ;
jpl:createdBy jpl:Person_123456 ;
jpl:organization “1234” .
jpl:Person_123456 rdfs:label “B. Schrader” .
IF
The first initial from data set 2 matches the first character of the first name in data set 1
AND
The last name from data set 2 matches the last name from data set 1
AND
The organization value from data set 2 matches the organization code of the
organization of which the person is a member
THEN The two entities are a match
• Alternative labels/educated
guesses (matching rules)
Probable
Linking Methods – Matching Rules
Extraction, or label matching, against the data already in the graph helps
with the transformation, allowing us to standardize/match references to
projects in one system to our existing URI for that project.
• Alternative labels/educated
guesses (matching rules)
Probable
Data Set 2
doc:5678 a jpl:Document ;
rdfs:label “OCO-2 Meeting Notes” ;
jpl:relatedProject project:OCO-2 .
project:OCO-2 rdfs:label “OCO-2” .
11/9/2022 Approved for Public Release: JPL CL#22-5900 38
Data Set 1
mission:OCO2 skos:prefLabel
“Orbiting Carbon Observatory 2” ;
skos:altLabel “Orbiting Carbon Observatory-2”,
“OCO 2”, “OCO2”, “OCO-2” .
Linking Methods – Manual Review
11/9/2022 Approved for Public Release: JPL CL#22-5900 39
Data Set 1
person:bschrader a ikg:Person ;
rdfs:label “Bess P Schrader” ;
ikg:username “bschrader” ;
ikg:firstName “Bess” ;
ikg:lastName “Schrader” ;
ikg:memberOf org:1234 .
person:schraderb a ikg:Person ;
rdfs:label “Bess X Schrader” ;
ikg:username “schraderb” ;
ikg:firstName “Bess” ;
ikg:lastName “Schrader” ;
ikg:memberOf org:5678 .
Data Set 2
doc:1234 a jpl:Document ;
rdfs:label “KM World 2022 Presentation” ;
jpl:createdBy jpl:Person_123456 .
jpl:Person_123456 rdfs:label “B. Schrader” ;
jpl:organization “1234” .
• Manual review
Sometimes
required
In some cases, the only option is
to manually reconcile entities.
Linking Methods – Manual Review
11/9/2022 Approved for Public Release: JPL CL#22-5900 40
Data Set 1
person:bschrader a ikg:Person ;
rdfs:label “Bess P Schrader” ;
ikg:username “bschrader” ;
ikg:firstName “Bess” ;
ikg:lastName “Schrader” ;
ikg:memberOf org:1234 .
person:schraderb a ikg:Person ;
rdfs:label “Bess X Schrader” ;
ikg:username “schraderb” ;
ikg:firstName “Bess” ;
ikg:lastName “Schrader” ;
ikg:memberOf org:5678 .
Data Set 2
doc:1234 a jpl:Document ;
rdfs:label “KM World 2022 Presentation” ;
jpl:createdBy jpl:Person_123456 .
jpl:Person_123456 rdfs:label “B. Schrader” ;
jpl:organization “1234” .
• Manual review
Sometimes
required
jpl:Person_123456 in data set 2 is
probably person:bschrader in data
set 1, not person:schraderb
Data Access Methods – Where does the linking happen?
11/9/2022 Approved for Public Release: JPL CL#22-5900 41
So we’ve found entity matches across our
data sets using our various linking
methods…now what?
We usually link data sets one of two ways
Linking
Methods
Copy data between
data sets
Leave data in place
and run federated
queries
Copy Data Method Example
11/9/2022 Approved for Public Release: JPL CL#22-5900 42
Data Set 1
person:bschrader a ikg:Person ;
rdfs:label “Bess P Schrader” .
Data Set 2
doc:1234 a jpl:Document ;
rdfs:label “KM World 2022 Presentation” ;
jpl:createdBy person:bschrader .
Data Set 2 - Augmented
doc:1234 a jpl:Document ;
rdfs:label “KM World 2022 Presentation” ;
jpl:createdBy person:bschrader .
person:bschrader a ikg:Person ;
rdfs:label “Bess P Schrader” .
Copying Data Based on Hooks – Labs and IKG
11/9/2022 Approved for Public Release: JPL CL#22-5900 43
Labs
Federated Query Example
11/9/2022 Approved for Public Release: JPL CL#22-5900 44
Data Set 1
person:bschrader a ikg:Person ;
rdfs:label “Bess P Schrader” .
Data Set 2
doc:1234 a jpl:Document ;
rdfs:label “KM World 2022 Presentation” ;
jpl:createdBy person:bschrader .
who created the KM World 2022 Presentation?
Data Set 2 KM World 2022 Presentation was created by
person:bschrader.
Data Set 1 person:bschrader has the name Bess P
Schrader.
Data Access Methods
– Where & when does the linking happen?
11/9/2022 Approved for Public Release: JPL CL#22-5900 45
Method Pros Cons
Copy data Ø Faster, straightforward queries
Ø More integration options
§ May be too much data for some tools
§ Requires synchronization to keep it up-to-
date (unless you want a snapshot in time)
§ Possibilities for failure/downtime - usual
processing risks
Federated
queries
Ø Access permissions can be separated
Ø Queries are real-time
§ Queries could be slower and more
complex
§ Could limit integration options with other
applications
Leveraging Symbiotic
Knowledge Graphs
46
11/9/2022 Approved for Public Release: JPL CL#22-5900
Now we can ask both simple and complex
questions
Enabling applications to enrich their data sets
11/9/2022 Approved for Public Release: JPL CL#22-5900 47
Connected data sets allow applications
to pull data in from across a variety of
data sets on demand
101
102
103 104
105
106
LAB INFO
Lab Name Lab X
Managing Org. Organization 1234
Lab Lead Sally Smith
Safety Coordinator William Safety
Facility Search
App
Enterprise Search
Enabling the ability to answer more complex queries
11/9/2022 Approved for Public Release: JPL CL#22-5900 48
By Org
• Which members of org 123 were active in
quality assurance activities over the past 3
months?
By Role
• Which mechanical engineers with
experience on rover missions left the Lab
this year?
By Project
• Which applications saw the most activity
leading up to the Critical Design Review
and in which applications?
By Person • Which lifecycle activities was J. Engineer
participating in that are still in progress?
By Topic
• What activity is currently in progress
involving robotics?
SPARQL
select *
{
service <repository:IKG> {?s ?p ?o}
service <repository:activity> {?s ?p ?o}
}
Enabling simple question-answer capability - natural
language queries so anyone can ask simple questions to
get answers
11/9/2022 Approved for Public Release: JPL CL#22-5900 49
Before we go
Approved for Public Release: JPL CL#22-5900 50
11/9/2022
skos:altLabel “In Conclusion”
Linking data sets takes effort …
11/9/2022 Approved for Public Release: JPL CL#22-5900 51
Thinking
ahead
Data
cleanup or
mitigation
steps
Semantic
momentum
but it’s worth it
“Standard” URIs are important – we might even say critical
11/9/2022 Approved for Public Release: JPL CL#22-5900 52
• Avoids name clashes - …/Records#Liaison vs …/Public_Relations#Liaison
• Identifies domain and data owner
Establish namespace owners for different data sets
• Enables ideal concept matching right away
• Enables federated queries
Use standard URIs from the get-go
• Keeps URI generation simple
• Usernames/employee IDs numbers
• Organization codes/identifiers
• Building numbers/location identifiers
• Matches consumer expectations
Reuse existing identifiers where possible
“Knowledge graphs are awesome.”
11/9/2022 Approved for Public Release: JPL CL#22-5900 53
enables to answer
Approved for Public Release: JPL CL#22-5900 54
11/9/2022

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  • 2. Who are we? 2 11/9/2022 Approved for Public Release: JPL CL#22-5900
  • 3. JPL: A NASA FFRDC Owned and Managed by Caltech Approved for Public Release: JPL CL#22-5900 3 11/9/2022
  • 4. Ann Bernath, Jet Propulsion Laboratory 1978 2015 2022 Taxonomies Ontologies Software Systems Engineering Document Management Word Processing Information Modeling Approved for Public Release: JPL CL#22-5900 4 11/9/2022
  • 5. Bess Schrader, Enterprise Knowledge employed by Information Science degree had internship focus focus taxonomy Linked data blew mind of Approved for Public Release: JPL CL#22-5900 5 11/9/2022
  • 6. Last Year at (virtual) KMWorld Approved for Public Release: JPL CL#22-5900 6 11/9/2022 Leveraging semantic technologies to add context to structured and unstructured data JPL’s Institutional Knowledge Graph
  • 7. Person info Institutional data § OCO 2 § OCO2 § OCO-2 § Orbiting Carbon Observatory-2 § Orbiting Carbon Observatory 2 We talked about our problem: multiple systems that maintain their own copies of enterprise data often resulting in out-of-sync data Approved for Public Release: JPL CL#22-5900 7 11/9/2022
  • 8. Institutional info & expertise was hard to find Approved for Public Release: JPL CL#22-5900 Who has a background in chemistry? Where’s the Robotics Lab? Who do I call if there’s a hazard? Who has experience on recent Mars lander missions? … 8 11/9/2022
  • 9. Desired state MSL Jane Brown Mars 2020 John Brown Mars Sample Return Red Green Who worked on recent Mars lander missions? Topics q Projects q Mars 2020 q Mission Targets q Mars q Organizations q Engineering and Science q Applications q Issue Tracking q Publications q Science q People … Institutional Knowledge Graph “One-stop shopping” Answers to questions Institutional Context Approved for Public Release: JPL CL#22-5900 9 11/9/2022
  • 10. JPL Domains (Business, Eng, Science) Institutional Layer (Organizations, People, Projects, Facilities, …) Institutional Model/Ontology W3C Syntax and Protocol Standards (OWL, RDF, RDFS, SHACL, SKOS, SPARQL, …) We realized we needed to build an institutional layer in our semantic pyramid Institutional layer was missing Approved for Public Release: JPL CL#22-5900 10 11/9/2022
  • 11. Our Solution: The Institutional Knowledge Graph Approved for Public Release: JPL CL#22-5900 M ission Targets Projects Locations P eo pl e 11 11/9/2022
  • 13. Our Expanding Semantic-Verse 13 11/9/2022 Approved for Public Release: JPL CL#22-5900
  • 14. JPL Domains (Business, Eng, Science) Institutional Knowledge Graph (Organizations, People, Projects, Facilities, …) Institutional Model/Ontology (Unique identifiers for institutional concepts) W3C Syntax and Protocol Standards (OWL, RDF, RDFS, SHACL, SKOS, SPARQL, …) Our semantic pyramid takes shape Domain specific knowledge graphs can build upon it Approved for Public Release: JPL CL#22-5900 14 11/9/2022 Now that we’ve laid this foundation
  • 15. Domain data sets are beginning to leverage our semantic foundation, enriching IKG in return 11/9/2022 Approved for Public Release: JPL CL#22-5900 15 Institutional Knowledge Graph Labs • Responsibilities • Facilities Master List of Controlled Records • Retention Schedules • Roles Business Event Transactions • Work Activity • Experience Infrastructure Technical Architecture Description • Functions • Systems • Services • Roles JPL Taxonomy • Real-World Concepts Science Taxonomy • Projects • Expertise
  • 16. For instance, the Labs data set enriches what the IKG knows about people, organizations, and locations 11/9/2022 Approved for Public Release: JPL CL#22-5900 16 Robotics Lab Jane Doe John Smith Robotics Design Organization Building 555 Room 000 Building 555 Room 000B responsible for accountable for managed by located in located in
  • 17. The IKG enriches lifecycle event data while lifecycle event data enriches what the IKG knows about a person’s expertise 11/9/2022 Approved for Public Release: JPL CL#22-5900 17
  • 18. Knowledge Graph Symbiosis 18 11/9/2022 Approved for Public Release: JPL CL#22-5900 Techniques to Federate or Synchronize Semantic Data Sets
  • 19. Why bother connecting data sets? 11/9/2022 Approved for Public Release: JPL CL#22-5900 19
  • 20. Connecting data from multiple systems is important not only does it allow us to connect the dots … 11/9/2022 Approved for Public Release: JPL CL#22-5900 20
  • 21. Connecting data from multiple systems is important …it also allows us to find discrepancies between different systems. For example, the IKG has found: Buildings appearing in one location application but not another People incorrectly listed as organization managers in HR system People that are no longer at JPL assigned to active roles 11/9/2022 Approved for Public Release: JPL CL#22-5900 21
  • 22. The technical nitty-gritty 11/9/2022 Approved for Public Release: JPL CL#22-5900 22
  • 23. Show of hands: who knows what a URI is? 11/9/2022 Approved for Public Release: JPL CL#22-5900 23 a lot of us only a few of us
  • 24. How do we encode this in a way that machines can understand it? Approved for Public Release: JPL CL#22-5900 24 11/9/2022 Let’s Pause for Some RDF Basics
  • 25. Resource Description Framework RDF – Resource Description Framework ● “Things not strings” ● W3C standard ● Model for data interchange on the web ● Allows integration of differing schemas or representations of data Approved for Public Release: JPL CL#22-5900 25 11/9/2022
  • 26. Robotics Lab John Smith accountable for Uniform Resource Identifiers (URI) To be machine readable, all of our bubbles and lines (i.e. the elements of our triple) need a Uniform Resource Identifier (URI). URIs are also known as IRIs (Internationalized Resource Identifier) URIs are unique identifiers that look like URLs (although they don’t actually have to go anywhere) Approved for Public Release: JPL CL#22-5900 26 11/9/2022
  • 28. Prefixes (aka Namespaces) <http://example.jpl.nasa.gov/ontologies/ikg/Lab/Robotics_Lab> lab @prefix lab: <http://example.jpl.nasa.gov/ontologies/ikg/Lab/> lab:Robotics_Lab <http://example.jpl.nasa.gov/ontologies/ikg/Lab/Robotics_Lab> Approved for Public Release: JPL CL#22-5900 28 11/9/2022
  • 29. lab:Robotics_Lab person:jsmith ikg:accountableFor Uniform Resource Identifiers (URI) Using prefixes, our Robotics Lab example would look like this: Approved for Public Release: JPL CL#22-5900 29 11/9/2022 @prefix ikg: <http://example.jpl.nasa.gov/ontologies/ikg#> @prefix lab: <http://example.jpl.nasa.gov/ontologies/ikg/Lab/> @prefix person: <http://example.jpl.nasa.gov/ontologies/ikg/Person/>
  • 30. Namespaces In addition to making URIs easier for humans to read, namespaces they can also help with establishing data ownership and governance. For example: ikg:accountableFor The “ikg” prefix indicates this relationship is owned by the Institutional Knowledge Graph (IKG). It may have a specific meaning in that context, and changes are controlled by the IKG team. hr:accountableFor The “hr” prefix indicates this relationship is owned by Human Resources. It may have a specific meaning in that context, and changes are controlled by the HR team. Approved for Public Release: JPL CL#22-5900 30 11/9/2022
  • 31. Uniform Resource Identifiers (URI) URIs are critical to building knowledge graphs, especially for ensuring that different semantic data sets can talk to each other. Reusing URIs across datasets that refer to the same concept helps ensure that: Approved for Public Release: JPL CL#22-5900 31 11/9/2022 entities only need to be defined once entities have a clear owner semantic data sets can be linked
  • 32. How to link data sets 11/9/2022 Approved for Public Release: JPL CL#22-5900 32
  • 33. Linking Methods 11/9/2022 Approved for Public Release: JPL CL#22-5900 33 • Re-use URIs for institutional entities enabling federated queries Ideal • Match on “hooks” (important properties) such as key identifiers (employee numbers, usernames, …) Pretty good • Alternative labels/educated guesses (matching rules) Probable • Manual review Sometimes required How do we match or link entities across different semantic data sets?
  • 34. Linking Methods – Reusing URIs 11/9/2022 Approved for Public Release: JPL CL#22-5900 34 • Use URIs for institutional entities enabling federated queries Ideal • Hooks such as key identifiers (employee numbers, usernames, …) Pretty good • Alternative labels/educated guesses (matching rules) Probable • Manual review Sometimes required Data Set 1 person:bschrader a ikg:Person ; rdfs:label “Bess P Schrader” . Data Set 2 doc:1234 a jpl:Document ; rdfs:label “KM World 2022 Presentation” ; jpl:createdBy person:bschrader . In the best case scenario, owners/creators of semantic data sets reuse URIs between data sets at the time of creation, so there’s no guess work involved in matching entities across data sets.
  • 35. Linking Methods – Using Hooks 11/9/2022 Approved for Public Release: JPL CL#22-5900 35 Data Set 1 person:bschrader a ikg:Person ; rdfs:label “Bess P Schrader” ; ikg:username “bschrader” . Data Set 2 doc:1234 a jpl:Document ; rdfs:label “KM World 2022 Presentation” ; jpl:createdBy jpl:Person_123456 . jpl:Person_123456 jpl:username “bschrader” . • Hooks such as key identifiers (employee numbers, usernames, …) Pretty good If the same URIs aren’t used across data sets, commonly used institutional identifiers (like usernames, department codes, etc.) can be another good option for finding entity matches.
  • 36. Linking Methods – Matching Rules 11/9/2022 Approved for Public Release: JPL CL#22-5900 36 Data Set 1 person:bschrader a ikg:Person ; rdfs:label “Bess P Schrader” ; ikg:username “bschrader” ; ikg:firstName “Bess” ; ikg:lastName “Schrader” ; ikg:memberOf org:1234 . org:1234 a ikg:Organization ; ikg:organizationCode “1234” . Data Set 2 doc:1234 a jpl:Document ; rdfs:label “KM World 2022 Presentation” ; jpl:createdBy jpl:Person_123456 ; jpl:organization “1234” . jpl:Person_123456 rdfs:label “B. Schrader” . • Alternative labels/educated guesses (matching rules) Probable Lacking re-used URIs or institutional identifiers, we often have to make up our own matching logic to determine if two entities are the same.
  • 37. Linking Methods – Matching Rules 11/9/2022 Approved for Public Release: JPL CL#22-5900 37 Data Set 1 person:bschrader a ikg:Person ; rdfs:label “Bess P Schrader” ; ikg:username “bschrader” ; ikg:firstName “Bess” ; ikg:lastName “Schrader” ; ikg:memberOf org:1234 . org:1234 a ikg:Organization ; ikg:organizationCode “1234” . Data Set 2 doc:1234 a jpl:Document ; rdfs:label “KM World 2022 Presentation” ; jpl:createdBy jpl:Person_123456 ; jpl:organization “1234” . jpl:Person_123456 rdfs:label “B. Schrader” . IF The first initial from data set 2 matches the first character of the first name in data set 1 AND The last name from data set 2 matches the last name from data set 1 AND The organization value from data set 2 matches the organization code of the organization of which the person is a member THEN The two entities are a match • Alternative labels/educated guesses (matching rules) Probable
  • 38. Linking Methods – Matching Rules Extraction, or label matching, against the data already in the graph helps with the transformation, allowing us to standardize/match references to projects in one system to our existing URI for that project. • Alternative labels/educated guesses (matching rules) Probable Data Set 2 doc:5678 a jpl:Document ; rdfs:label “OCO-2 Meeting Notes” ; jpl:relatedProject project:OCO-2 . project:OCO-2 rdfs:label “OCO-2” . 11/9/2022 Approved for Public Release: JPL CL#22-5900 38 Data Set 1 mission:OCO2 skos:prefLabel “Orbiting Carbon Observatory 2” ; skos:altLabel “Orbiting Carbon Observatory-2”, “OCO 2”, “OCO2”, “OCO-2” .
  • 39. Linking Methods – Manual Review 11/9/2022 Approved for Public Release: JPL CL#22-5900 39 Data Set 1 person:bschrader a ikg:Person ; rdfs:label “Bess P Schrader” ; ikg:username “bschrader” ; ikg:firstName “Bess” ; ikg:lastName “Schrader” ; ikg:memberOf org:1234 . person:schraderb a ikg:Person ; rdfs:label “Bess X Schrader” ; ikg:username “schraderb” ; ikg:firstName “Bess” ; ikg:lastName “Schrader” ; ikg:memberOf org:5678 . Data Set 2 doc:1234 a jpl:Document ; rdfs:label “KM World 2022 Presentation” ; jpl:createdBy jpl:Person_123456 . jpl:Person_123456 rdfs:label “B. Schrader” ; jpl:organization “1234” . • Manual review Sometimes required In some cases, the only option is to manually reconcile entities.
  • 40. Linking Methods – Manual Review 11/9/2022 Approved for Public Release: JPL CL#22-5900 40 Data Set 1 person:bschrader a ikg:Person ; rdfs:label “Bess P Schrader” ; ikg:username “bschrader” ; ikg:firstName “Bess” ; ikg:lastName “Schrader” ; ikg:memberOf org:1234 . person:schraderb a ikg:Person ; rdfs:label “Bess X Schrader” ; ikg:username “schraderb” ; ikg:firstName “Bess” ; ikg:lastName “Schrader” ; ikg:memberOf org:5678 . Data Set 2 doc:1234 a jpl:Document ; rdfs:label “KM World 2022 Presentation” ; jpl:createdBy jpl:Person_123456 . jpl:Person_123456 rdfs:label “B. Schrader” ; jpl:organization “1234” . • Manual review Sometimes required jpl:Person_123456 in data set 2 is probably person:bschrader in data set 1, not person:schraderb
  • 41. Data Access Methods – Where does the linking happen? 11/9/2022 Approved for Public Release: JPL CL#22-5900 41 So we’ve found entity matches across our data sets using our various linking methods…now what? We usually link data sets one of two ways Linking Methods Copy data between data sets Leave data in place and run federated queries
  • 42. Copy Data Method Example 11/9/2022 Approved for Public Release: JPL CL#22-5900 42 Data Set 1 person:bschrader a ikg:Person ; rdfs:label “Bess P Schrader” . Data Set 2 doc:1234 a jpl:Document ; rdfs:label “KM World 2022 Presentation” ; jpl:createdBy person:bschrader . Data Set 2 - Augmented doc:1234 a jpl:Document ; rdfs:label “KM World 2022 Presentation” ; jpl:createdBy person:bschrader . person:bschrader a ikg:Person ; rdfs:label “Bess P Schrader” .
  • 43. Copying Data Based on Hooks – Labs and IKG 11/9/2022 Approved for Public Release: JPL CL#22-5900 43 Labs
  • 44. Federated Query Example 11/9/2022 Approved for Public Release: JPL CL#22-5900 44 Data Set 1 person:bschrader a ikg:Person ; rdfs:label “Bess P Schrader” . Data Set 2 doc:1234 a jpl:Document ; rdfs:label “KM World 2022 Presentation” ; jpl:createdBy person:bschrader . who created the KM World 2022 Presentation? Data Set 2 KM World 2022 Presentation was created by person:bschrader. Data Set 1 person:bschrader has the name Bess P Schrader.
  • 45. Data Access Methods – Where & when does the linking happen? 11/9/2022 Approved for Public Release: JPL CL#22-5900 45 Method Pros Cons Copy data Ø Faster, straightforward queries Ø More integration options § May be too much data for some tools § Requires synchronization to keep it up-to- date (unless you want a snapshot in time) § Possibilities for failure/downtime - usual processing risks Federated queries Ø Access permissions can be separated Ø Queries are real-time § Queries could be slower and more complex § Could limit integration options with other applications
  • 46. Leveraging Symbiotic Knowledge Graphs 46 11/9/2022 Approved for Public Release: JPL CL#22-5900 Now we can ask both simple and complex questions
  • 47. Enabling applications to enrich their data sets 11/9/2022 Approved for Public Release: JPL CL#22-5900 47 Connected data sets allow applications to pull data in from across a variety of data sets on demand 101 102 103 104 105 106 LAB INFO Lab Name Lab X Managing Org. Organization 1234 Lab Lead Sally Smith Safety Coordinator William Safety Facility Search App Enterprise Search
  • 48. Enabling the ability to answer more complex queries 11/9/2022 Approved for Public Release: JPL CL#22-5900 48 By Org • Which members of org 123 were active in quality assurance activities over the past 3 months? By Role • Which mechanical engineers with experience on rover missions left the Lab this year? By Project • Which applications saw the most activity leading up to the Critical Design Review and in which applications? By Person • Which lifecycle activities was J. Engineer participating in that are still in progress? By Topic • What activity is currently in progress involving robotics? SPARQL select * { service <repository:IKG> {?s ?p ?o} service <repository:activity> {?s ?p ?o} }
  • 49. Enabling simple question-answer capability - natural language queries so anyone can ask simple questions to get answers 11/9/2022 Approved for Public Release: JPL CL#22-5900 49
  • 50. Before we go Approved for Public Release: JPL CL#22-5900 50 11/9/2022 skos:altLabel “In Conclusion”
  • 51. Linking data sets takes effort … 11/9/2022 Approved for Public Release: JPL CL#22-5900 51 Thinking ahead Data cleanup or mitigation steps Semantic momentum but it’s worth it
  • 52. “Standard” URIs are important – we might even say critical 11/9/2022 Approved for Public Release: JPL CL#22-5900 52 • Avoids name clashes - …/Records#Liaison vs …/Public_Relations#Liaison • Identifies domain and data owner Establish namespace owners for different data sets • Enables ideal concept matching right away • Enables federated queries Use standard URIs from the get-go • Keeps URI generation simple • Usernames/employee IDs numbers • Organization codes/identifiers • Building numbers/location identifiers • Matches consumer expectations Reuse existing identifiers where possible
  • 53. “Knowledge graphs are awesome.” 11/9/2022 Approved for Public Release: JPL CL#22-5900 53 enables to answer
  • 54. Approved for Public Release: JPL CL#22-5900 54 11/9/2022