15 mins presentation at NZ eResearch symposium 2013 illustrating my current PhD research

1. Capturing
the
Flux
in
Scienti2ic
Knowledge
Centre
for
eResearch
Dept.
of
Computer
Science
University
of
Auckland
Prashant
Gupta
(PhD
student)
Mark
Gahegan

2. “The
flux
of
things
is
one
ul0mate
generaliza0on
around
which
we
must
weave
our
philosophical
system.”
hBp://smeitexpo2011.blogspot.co.nz/2010/11/era-­‐of-­‐technological-­‐revoluLon.html
-­‐-­‐Alfred
N.
Whitehead,
Process
and
Reality

3. Example…
v Paradigm
shiR
Wave-­‐parLcle
Duality
18th
Century
–
Light
as
material
corpuscles
Early
20th
Century
–
Light
as
wave
parLcles

4. Incremental
changes
v Constant
reorganizaLon
of
PhylogeneLc
tree
hBp://www.wiley.com/college/praB/0471393878/student/acLviLes/phylogeneLc_trees/

5. Incremental
changes
v Constant
reorganizaLon
of
PhylogeneLc
tree
v New
ObservaLon/data
v New
Understanding
v Societal
drivers
hBp://www.wiley.com/college/praB/0471393878/student/acLviLes/phylogeneLc_trees/

6. How
do
we
currently
handle
the
“Change”
v Schema
EvoluLon
(Databases
and
XML)
/
Ontology
EvoluLon
Level
of
abstracLon
v CategorizaLon
Complexity-based
Complex
Composite
Atomic
v Provenance
/
Change
Logs
Domain-­‐
specific

7. Example
of
an
ontology
change
log
It
tells
us
Knowledge-­‐that:
what
is
the
change,
when
it
happened,
who
did
it,
what
was
the
target,
etc..
M.
Javed,
Y.
M.
Abgaz,
and
C.
Pahl,
“Ontology
Change
Management
and
IdenLficaLon
of
Change
PaBerns,”
J
Data
Semant,
May
2013.

8. How
did
this
change
came
into
being?
Example
of
an
ontology
change
log
It
tells
us
Knowledge-­‐that:
what
is
the
change,
when
it
happened,
who
did
it,
what
was
the
target,
etc..
But
we
sLll
miss
Knowledge-­‐how
(and
why)
M.
Javed,
Y.
M.
Abgaz,
and
C.
Pahl,
“Ontology
Change
Management
and
IdenLficaLon
of
Change
PaBerns,”
J
Data
Semant,
May
2013.
Why
did
they
make
that
decision?

9. ScienLfic
Enterprise
Theories,
Laws
etc.
Conceptual
Model
ApplicaLons
e.g.
Maps
Data
Model
Categories
hBp://sLck.ischool.umd.edu/innovaLon_ontology.html
Process
Model

10. ScienLfic
Enterprise
Theories,
Laws
etc.
Conceptual
Model
ApplicaLons
e.g.
Maps
Data
Model
Categories
Ontology
Database
hBp://sLck.ischool.umd.edu/innovaLon_ontology.html
Process
Model
Workflow

11. ScienLfic
Enterprise
Theories,
Laws
etc.
Conceptual
Model
ApplicaLons
e.g.
Maps
Data
Model
Categories
Ontology
Database
hBp://sLck.ischool.umd.edu/innovaLon_ontology.html
Process
Model
Workflow

12. ScienLfic
Enterprise
Theories,
Laws
etc.
Conceptual
Model
ApplicaLons
e.g.
Maps
Data
Model
Categories
affects
Change
hBp://sLck.ischool.umd.edu/innovaLon_ontology.html
Process
Model

13. ScienLfic
Enterprise
Theories,
Laws
etc.
Conceptual
Model
ApplicaLons
e.g.
Maps
Data
Model
Process
Model
Categories
Change
Categories
Categories
Categories
affects
Change
hBp://sLck.ischool.umd.edu/innovaLon_ontology.html

14. Life-­‐Cycle
of
a
Category

15. Life-­‐Cycle
of
a
Category
Birth
of
a
category
Data
Processes
Theory
Contexts/
Researchers’
SituaLons
knowledge
Category
Place
in
Intension
Extension
Conceptual
hierarchy

16. Life-­‐Cycle
of
a
Category
Birth
of
a
category
Data
Processes
Theory
Contexts/
Researchers’
SituaLons
knowledge
Category
Place
in
Intension
Extension
Conceptual
hierarchy
Conceptual
change
May
lead
to
new
understanding
May
cause
change
to
exisLng
theory
New
observaLons
Societal
needs
Richer
characterizaLon
Category
Place
in
Intension
Extension
Conceptual
hierarchy
EvoluLon
of
a
category

17. How
can
we
answer
How
and
why
aspect
of
change
?
Change
What
knowledge
are
we
missing
!

18. How
can
we
answer
How
and
why
aspect
of
change
?
What
knowledge
are
we
missing
!
Change
We
focus
on
products
of
science
and
ignore
process
of
science

19. What’s
in
the
process!
v Source
of
interpretaLon
v Can
answer
quesLons
related
to
how
and
why
aspect
behind
the
change

20. Proposed
Solution
Now
I
understand
why
this
category
is
the
way
it
is…
Categories
Process
of
science

21. Conceptual
Signi2icance
v Fourth
facet
to
a
category’s
representaLon
v Address
the
informaLon
interoperability
problem
v BeBer
understanding
of
how
our
scienLfic
knowledge
evolves
over
Lme

22. Process
of
Science
give
birth
to
improve
Conceptual
Change
ScienLfic
ArLfacts
connected
as
Workflow
Database
modify
Ontology
ApplicaLon

23. Computational
Framework
Service
1
Service
2
Service
3
Change
Analyzer
Change
event
Categorical
templates
• Recording
changes
and
processes
involved
• Analyze
changes
• Broadcast
changes
Machine-­‐learning
techniques
• Neural
networks
• Bayesian
Network
…….
Category-­‐versioning
system
stub
stub
Change
event

24. Computational
Framework
Service
1
Change
Analyzer
Change
event
Categorical
templates
• Recording
changes
and
processes
involved
• Analyze
changes
• Broadcast
changes
Machine-­‐learning
techniques
• Neural
networks
• Bayesian
Network
…….
Category-­‐versioning
system
stub
stub
Change
event

25. Computational
Framework
Service
1
Data-­‐based
Change
event
•
•
•
•
•
Dataset
Training
set
Categorical
Classifier
templates
Parameters
ValidaLon
method
Change
Analyzer
• Recording
changes
and
processes
involved
• Analyze
changes
• Broadcast
changes
Machine-­‐learning
techniques
• Neural
networks
• Bayesian
Network
…….
Category-­‐versioning
system
stub
stub
Change
event

26. Computational
Framework
Service
1
Change
Analyzer
Change
event
Categorical
templates
• Recording
changes
and
processes
involved
• Analyze
changes
• Broadcast
changes
Machine-­‐learning
techniques
• Neural
networks
• Bayesian
Network
…….
Category-­‐versioning
system
stub
stub
Change
event

27. Computational
Framework
Service
2
Change
Analyzer
Change
event
Categorical
templates
• Recording
changes
and
processes
involved
• Analyze
changes
• Broadcast
changes
Machine-­‐learning
techniques
• Neural
networks
• Bayesian
Network
…….
Category-­‐versioning
system
stub
stub
Change
event

28. Computational
Framework
Service
3
Change
Analyzer
Change
event
Categorical
templates
• Recording
changes
and
processes
involved
• Analyze
changes
• Broadcast
changes
Machine-­‐learning
techniques
• Neural
networks
• Bayesian
Network
…….
Category-­‐versioning
system
stub
stub
Change
event

29. Questions
??
Thanks
to
Mark
Gahegan
(Supervisor)
Gill
Dobbie
(co-­‐supervisor)
CeR
Fellows
Prashant
Gupta
PhD
student
p.gupta@auckland.ac.nz