This document outlines a webinar focused on F.A.I.R data principles and their application in knowledge graphs and AI strategies. Key topics include standards for quality data, the importance of keeping humans involved in data processes, and how ontologies enhance business context. The document also discusses the integration of semantic AI tools and the significance of effective data governance.

1. F.A.I.R Data with Knowledge
Graphs & AI
Strategy, processes & practices, and tools

2. Who is speaking today?
James Morris
Senior Informaiton
Scientist
Smartlogic
Fredric Landqvist
Senior Information
Architect
TietoEvry/Findwise

3. A few housekeeping items
• This webinar is in broadcast mode all
participants are muted
• Please put your questions in the
GoToWebinar panel and we’ll answer
as many as we can in the Q & A
session
• This broadcast is being recorded –
replay information will be sent to all
registrants following the broadcast

4. Agenda
• The quest for quality data and F.A.I.R principles
• Available standards and opportunities
• Mitigation opportunities – keeping the human in the loop
• How ontologies, semantics and enterprise knowledge graphs
provide a connection to business context
• Q&A

5. Data Governance
The quest for quality data assets!

6. F.A.I.R

7. 5 - Star

8. Knowledge Graphs

9. Alphabet Soup
Vocabularies
http://flic.kr/p/zCyMp
Standards
Knowledge
Models

10. Cherry Picking
http://flic.kr/p/8Xtus2

11. Organising

12. Automatic for the people
Digital Assistants (AI)
Machine Learning
NLP
Federated Learning
…

13. Societal
Data
Challenges
Pathogen
Infrastructure
Environment

14. Health Data
The connected patient

15. Pathogen
• Patient, Profession,
Politics, Provision,
Provenance, Process,
Participation & Patterns
• Problem: Data chasm!

16. Smart Society Data
• Smart Cities, Smart
Building, next
generation
engineering, IoT,
infrastructure
• Standards: BRICK,
BOT, OSCL,
RealEstateCore,
SAREF…

17. Digital Blue Economy
Ocean Data Factory

19. © 2021 SMARTLOGIC SEMAPHORE INC.
F.A.I.R. principles in Context
FINDABLE
ACCESSIBLE
INTEROPERABLE
RE-USABLE
→ FAIR in the context of Information
Architecture
→ FAIR in the context of Librarianship
→ FAIR in the context of Smartlogic experience…
o GXP and research organizations
o Library/Informatics teams within
organizations
→ FAIR in the context of Knowledge Graphs and
Semantic AI

20. © 2021 SMARTLOGIC SEMAPHORE INC.
F.A.I.R. in information architecture
20
https://xd.adobe.com/ideas/process/information-architecture/information-architecture-users-content-context/
. Retrieved 25-Apr-2021

21. © 2021 SMARTLOGIC SEMAPHORE INC.
F.A.I.R. in the Librarians’ Context
https://www.mhpbooks.com/library-of-congress-quiz-for-librarians-and-also-regular-people/moby-card-
catalog/

22. © 2021 SMARTLOGIC SEMAPHORE INC.
F.A.I.R. in the Librarians’ Context
22
+
+
Then
Now
= F.A.I.R.

23. © 2021 SMARTLOGIC SEMAPHORE INC. 23
RDA/Objectives and Principles/Rev/3 1 July 2009
FIND IDENTIFY
SELECT
OBTAIN
UNDERSTAND
“The (meta)data should enable the user to…”

24. © 2021 SMARTLOGIC SEMAPHORE INC.
The 5 Laws of Library Science
1. Books Are For Use
2. Every Reader His/Her Book
3. Every Book Its Reader
4. Save The Time Of The Reader
5. The Library Is A Growing Organism
S. R. Ranganathan, 1931.
© Vikas Kamat

25. © 2021 SMARTLOGIC SEMAPHORE INC.
The 5 FAIR Laws of Data Science?
1. Data is for use by researchers*
2. Every researcher* their data
3. Every data object its researcher*
4. Save the time of the researcher*
5. The world of research data is a growing organism
*or their machine agents
https://www.develandoo.com/blog/do-robots-read/

26. © 2021 SMARTLOGIC SEMAPHORE INC.
F.A.I.R. and the Semantic Web?
https://5stardata.info/en/
F.A.I.R.

27. © 2021 SMARTLOGIC SEMAPHORE INC.
F.A.I.R. in the context of Smartlogic clients
27
https://www.ideagen.com/thought-leadership/blog/how-to-meet-all-9-alcoa-
principles-with-our-document-module

28. © 2021 SMARTLOGIC SEMAPHORE INC.
ACOLA+ data integrity principles
28
→ #1: Attributable: The person who performs a
data-related task must be identifiable as the
person who performed that task.
→ #2: Legible: Data should be readable and
understandable, with a clear picture of the
step/event sequence that data has passed
through
→ #3: Contemporaneous:
Data activity should be time stamped with a
record of when it took place.
→ #4: Original: Every originally captured piece of
data must be retained, rather than replaced or
deleted.
→ #5: Accurate: Data should be inputted, stored
and maintained with precision and validity.
https://www.ideagen.com/thought-leadership/blog/how-to-meet-all-9-alcoa-principles-with-our-document-module
→ #6: Complete: Data features a trackable audit
trail to prove that nothing has been deleted or
lost.
→ #7: Consistent: Data should display consistently,
wherever it is accessed from within your
document management system.
→ #8: Enduring: Records and information should
be accessible and readable during the entire
period in which they might be needed...
potentially decades after recording!
→ #9: Available: Documents and records should be
accessible in a readable format to all applicable
personnel responsible for their review or
operational processes. External users should also
be provided access for inspection/review where
necessary.

29. © 2021 SMARTLOGIC SEMAPHORE INC.
ACOLA+ and F.A.I.R.
29
For primary purpose of data:
GXP of product development, approval, and
distribution
For secondary use of data:
Research, data science, collaborations, new
opportunities

30. © 2021 SMARTLOGIC SEMAPHORE INC.
F.A.I.R. and Smartlogic Life Sciences Clients
30
FINDABLE
ACCESSIBLE
INTEROPERABLE
RE-USABLE
● Build a FAIR culture
○ Reference it in strategic plans
○ Meet people where they are
○ Capitalize on the current momentum
● Provide the right infrastructure to be FAIR
● Provide tools that integrate easily with their existing systems
● Make it easy to choose the right metadata:
○ Don’t over complicate the vocabularies
○ Map to vocabularies already in use
○ Provide assistance with choosing the right values
○ Automatically assign values when possible.
● Support the process with services
● Make it easy to do the right thing!

31. © 2021 SMARTLOGIC SEMAPHORE INC.
• Trending: used for knowledge representation and reasoning for by leaders
like Facebook, Google, Microsoft and any organization dependent on rapidly
changing, interconnected data.
• “Schema-less”: new data and types can be easily incorporated as there is no
formal structure to which the information must comply. Being non-
relational, information is stored as a series of nodes and edges or simple
constructs of subject-predicate-object (triple).
• Dynamic: a growing semantic network of facts about things that can be used
for data integration, knowledge discovery, and in-depth analysis.
• Intelligent? a successful graph combines data from many different sources
allowing for new connections to made, inferred or tested. Ontologies or
other knowledge models help make those connections and pose hypotheses.
• Chaotic: Data in a graph does not automatically form connections and can
lead to user frustration. Planning, intention, and a focus on semantics is
needed.
Knowledge Graphs are…
31

32. © 2021 SMARTLOGIC SEMAPHORE INC.
Semaphore in a nutshell
32
Semaphore delivers these capabilities at enterprise scale
Build and manage semantic models
Simplify the ingestion, development and customization
Enrich, extract and harmonize
• Enrich information assets with complete, consistent and precise metadata
• Extract critical facts, entities and relationships for further processing
• Harmonize different information sources for unified access
Apply semantics to your business problem
• Enable knowledge discovery
• Support investigative analytics
• Automate manual processes for higher precision

33. © 2021 SMARTLOGIC SEMAPHORE INC.
Semaphore and Knowledge Graphs
DATA FROM
DOCUMENTS
DATA FROM
DATABASES
ONTOLOGIES AND
KNOWLEDGE MODELS

34. © 2021 SMARTLOGIC SEMAPHORE INC.
Discovering Knowledge with Graph Traversal
34

35. © 2021 SMARTLOGIC SEMAPHORE INC. 35
Knowledge Graphs in a Wider Context.
Reference
Data
Semantic Data
Extraction
Semantically-Enhanced
Data Catalogue
Metadata Hub
Subjective Enrichment
(Aboutness)
Semantically-Enhanced
Analytics
Enterprise
Knowledge
Graph
Semaphore Semantic AI Services, Integrations and Capabilities
Semantic Search
& Discovery

36. Q & A
Thank you for attending
We’ll take questions now
Fredric Landqvist & James R Morris
Findwise /TietoEvry & Smartlogic

37. References and links
• F.A.I.R Data principles and Open Phacts (open pharma space)
• FORCE11 - Future of Research Communications and e-Scholarship
• Open Standards, Knowledge Models and Vocabularies:
• W3C RDF, SKOS, OWL, SHACL
• Health Data: HL7/FHIR, MeSH, SnoMed CT, UMLS, ICD11 , OMG and more
• Smart Cities, Buildings and Services: BRICKS, BOT, SANREF, OSLC
• Information Science: Resource Description and Access (RDA), ACOLA+
• Blog post-series on FAIR Data, Knowledge Graphs, AI and more
• Findwise, TietoEvry and Smartlogic

38. Thank You