JPL’s Institutional Knowledge Graph II: A Foundation for Constructing Enterprise and Domain-Specific Semantic Data Sets

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
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Ann Bernath, Jet Propulsion Laboratory
1978 2015 2022
Taxonomies
Ontologies
Software
Systems
Engineering
Document
Management
Word
Processing
Information
Modeling
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Bess Schrader, Enterprise Knowledge
employed by
Information Science
degree
had internship
focus
focus
taxonomy
Linked data
blew mind
of
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Last Year at (virtual) KMWorld
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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
11/9/2022

Institutional info & expertise was hard to find
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
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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
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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
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Our Solution:
The Institutional
Knowledge
Graph
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
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IKG History Graph

Our Expanding Semantic-Verse
13

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
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
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

Knowledge Graph Symbiosis
18
Techniques to Federate or Synchronize
Semantic Data Sets

Why bother connecting data
sets?

Connecting data from multiple systems is important
not only does it
allow us to connect
the dots …

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

The technical nitty-gritty

Show of hands:
who knows what a URI is?
a lot of us only a few of us

How do we encode this in a way that machines can understand it?
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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
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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)
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<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>
Using URIs, our Robotics Lab example would look like this:
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Prefixes (aka Namespaces)
lab
@prefix lab: <http://example.jpl.nasa.gov/ontologies/ikg/Lab/>
lab:Robotics_Lab
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lab:Robotics_Lab
person:jsmith
ikg:accountableFor
Using prefixes,
our Robotics Lab
example would
look like this:
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@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.
11/9/2022

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:
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entities only need to be defined once
entities have a clear owner
semantic data sets can be linked

How to link data sets

Linking Methods
• 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
• 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
Data Set 1
rdfs:label “Bess P Schrader” ;
ikg:username “bschrader” .
Data Set 2
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
Data Set 1
ikg:username “bschrader” ;
ikg:firstName “Bess” ;
ikg:lastName “Schrader” ;
ikg:memberOf org:1234 .
org:1234 a ikg:Organization ;
ikg:organizationCode “1234” .
Data Set 2
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.

Data Set 1
org:1234 a ikg:Organization ;
ikg:organizationCode “1234” .
Data Set 2
jpl:createdBy jpl:Person_123456 ;
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
Probable

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.
Probable
Data Set 2
rdfs:label “OCO-2 Meeting Notes” ;
jpl:relatedProject project:OCO-2 .
project:OCO-2 rdfs:label “OCO-2” .
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
Data Set 1
person:schraderb a ikg:Person ;
rdfs:label “Bess X Schrader” ;
ikg:username “schraderb” ;
Data Set 2
jpl:Person_123456 rdfs:label “B. Schrader” ;
• Manual review
Sometimes
required
In some cases, the only option is
to manually reconcile entities.

Linking Methods – Manual Review
Data Set 1
person:schraderb a ikg:Person ;
rdfs:label “Bess X Schrader” ;
ikg:username “schraderb” ;
Data Set 2
jpl:Person_123456 rdfs:label “B. Schrader” ;
• 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?
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
Data Set 1
Data Set 2
Data Set 2 - Augmented

Copying Data Based on Hooks – Labs and IKG
Labs

Federated Query Example
Data Set 1
Data Set 2
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?
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
Now we can ask both simple and complex
questions

Enabling applications to enrich their data sets
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
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

Before we go
11/9/2022
skos:altLabel “In Conclusion”

Linking data sets takes effort …
Thinking
ahead
Data
cleanup or
mitigation
steps
Semantic
momentum
but it’s worth it

“Standard” URIs are important – we might even say critical
• 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.”
enables to answer

11/9/2022

JPL’s Institutional Knowledge Graph II: A Foundation for Constructing Enterprise and Domain-Specific Semantic Data Sets

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