How are personal knowledge graphs evolving? What functionality should they include? What are some of the examples of emerging PKG platforms?

1. For All KGC2021 Attendees Prepared by Alan Morrison Version 1.0
Paths to More Personal
and Collaborative
Knowledge Graphs
A KGC2021 Workshop

2. Version 1.0
For All KGC2021 Attendees Prepared by Alan Morrison
Agenda
Overview (Alan)
Notetaking and PKGs (Ivo Velitchkov)
SOLID and PKGs (Chu Nnodu)
Discussion/Q&A
Roam and Notetaking (Conor White-Sullivan)
RDF, Notetaking and PKGs (Brian J. Rubinton)
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3. Goal of today’s workshop
Envision a future for personal knowledge graphs (PKGs).
● PKGs are not mature--so this “workshop” won’t be a tutorial, but rather a brainstorming session.
● First question: How do we make PKGs easier for users?
● Second question: How do we get more math in the graph and not overwhelm users at the same time?
● Third question: How do we harness the momentum of the Networked Notetaking and Personal Data Protection
movements?
● Hypothesis: PKGs could provide an on-ramp for users who want to help build business knowledge graphs too.
Attendees are encouraged to join today’s discussion. We’ll keep things informal, so feel free to ask questions throughout.
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4. What’s a knowledge graph? (1 of 2)
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2
Data (including content) that’s
contextualized and disambiguated with the
help of definition language and description
logic
3
Rich, diverse data (including content) that’s
both machine- and human-readable
4
A means of creating digital twins:
Representations of people, places, things
and other concepts and how they’re
logically interrelated in graph form
Natural language, numerical data,
multimedia data and logic blended
together
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The bigger knowledge graphs (DBpedia, Wikidata, KBpedia, Blue Brain, Diffbot,Tech Sector KGs) do
more of these things

5. What’s a knowledge graph? (2 of 2)
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A large-scale, rapid means of integration
and even interoperation
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A platform in which symbolic logic
(language, reasoning and abstraction) can
meld with statistical machine learning
(perception and learning)
6
A means of model-driven software
development
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6. What’s a personal knowledge graph?
1
A knowledge graph for personal data
(including content)
2
A means of personal data (and knowledge)
management
3
A (potentially) radical journaling, creativity
and productivity tool
4
A starting point for decentralizing personal
data protection
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Personal knowledge graphs are in their infancy, and a primary focus has to be on ease of use

7. Logic: A key differentiator in future knowledge
graphs
Source: Dr. Leo Obrst, Mitre; Mills Davis, Project 10X; RAI,
2010-2021
Strong
Semantics
Weak
Semantics
Quantum Logic
Mathematical
logic
Ontology
Taxonomy
List
Folksonomy
Controlled Vocabulary
Glossary
Relational Model, XML
DB Schema, XML Schema
ER Model
Topic Map
RDF/S
UML
OWL
Description Logic
First Order Logic
Modal Logic
2nd
Order Logic
Higher Order
Logic
Recovery Discovery Intelligence Question answering Smart Behaviors
Increasing Reasoning Capability
Semantic
Interoperability
Structural
Interoperability
Syntactic
Interoperability
Increasing
Metadata,
Context,
&
Knowledge
Representation
Datalog
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8. What’s networked
notetaking?
[[Roam]]-->
is an example
https://roamresearch.com/ 8

9. What’s SOLID (Social Linked Data)?
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An open means of web decentralization
2
Specs for a decentralized web API
3
Decentralized server specs and a related
service (SOLID pods) that allow users to
store and share their data securely
4
A starting point for decentralizing personal
data protection
Chu Nnodu will provide a demo and more details
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10. Why do we care about decentralization?
● Personal knowledge graphs need to put personal data ownership and protection first
● Users want services that work across application silos or social media walled gardens
(e.g., the BBC’s music recommendation engine that works across Spotify, BBC Music,
etc.)
● Users that maintain personally-identifiable information (PII) in pods or hubs can
prevent duplication of their PII and verify credentials without having to share
correlatable identifiers
● PII controlled and stored by users in their own pods or hubs
● To verify credentials, businesses can avoid storing PII
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11. Why do we care about semantics, relationships
and dynamic graph data models?
● How and why things are
connected is extremely
important to describe
context
● Ontologies, inferencing and
first-order logic take us
further in bridging the gap
between human and
machine intelligence
● Personal knowledge graphs
need just enough
semantics
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12. Networked notetaking with Linked Open
Data/RDF-style capabilities
Kanopi.io
is Brian Rubinton’s
example→
And Ivo Velitchkov’s
Roam augmentation
is another
https://kanopi.io/
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13. Work in Progress: A clear path to a personal
knowledge graph
● A simple version of a semantic graph
● A rich means of easy integration
● A way to manage and control all sorts of personal data (including content)
● Bridges to networked notetaking and the open web
● Decentralized storage and data security that favors individual users
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Objectives

14. For All KGC2021 Attendees Prepared by Alan Morrison Version 1.0
Questions?
Alan Morrison
LinkedIn | Twitter | Quora | Slideshare
+1 408 205 5109
a.s.morrison@gmail.com
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