NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
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Capturing the Flux in Scienti2ic Knowledge Centre for eResearch Dept. of Computer Science University of Auckland Prashant Gupta (PhD student) Mark Gahegan
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15 mins presentation at NZ eResearch symposium 2013 illustrating my current PhD research
Transcript of NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
Capturing the Flux in Scienti2ic Knowledge
Centre for eResearch Dept. of Computer Science University of Auckland
Prashant Gupta (PhD student) Mark Gahegan
hBp://smeitexpo2011.blogspot.co.nz/2010/11/era-‐of-‐technological-‐revoluLon.html
“The flux of things is one ul0mate generaliza0on around which we must weave our philosophical system.” -‐-‐Alfred N. Whitehead, Process and Reality
Example…
v Paradigm shiR
Wave-‐parLcle Duality
18th Century – Light as material corpuscles
Early 20th Century – Light as wave parLcles
Incremental changes v Constant reorganizaLon of PhylogeneLc tree
hBp://www.wiley.com/college/praB/0471393878/student/acLviLes/phylogeneLc_trees/
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/
How do we currently handle the “Change”
v Schema EvoluLon (Databases and XML) / Ontology EvoluLon
v CategorizaLon
v Provenance / Change Logs
Domain-‐specific
Composite
Complex
Atomic
Complexity-based
Level of abstracLon
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.
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..
Why did they make that decision?
How did this change came into being?
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.
Categories
Conceptual Model Data Model Process
Model
Theories, Laws etc.
ApplicaLons e.g. Maps
ScienLfic Enterprise
hBp://sLck.ischool.umd.edu/innovaLon_ontology.html
Categories
Conceptual Model Data Model Process
Model
Theories, Laws etc.
ApplicaLons e.g. Maps
ScienLfic Enterprise
Ontology Workflow
Database
hBp://sLck.ischool.umd.edu/innovaLon_ontology.html
Categories
Conceptual Model Data Model Process
Model
Theories, Laws etc.
ApplicaLons e.g. Maps
ScienLfic Enterprise
Ontology Workflow
Database
hBp://sLck.ischool.umd.edu/innovaLon_ontology.html
Change
Categories
Conceptual Model Data Model Process
Model
Theories, Laws etc.
ApplicaLons e.g. Maps
ScienLfic Enterprise
affects
hBp://sLck.ischool.umd.edu/innovaLon_ontology.html
Change
Categories
Conceptual Model Data Model Process
Model
Theories, Laws etc.
ApplicaLons e.g. Maps
ScienLfic Enterprise
affects
Categories
Categories
Categories
Change
hBp://sLck.ischool.umd.edu/innovaLon_ontology.html
Life-‐Cycle of a Category
Life-‐Cycle of a Category
Theory
Data Category
Intension Extension
Place in Conceptual hierarchy
Processes Contexts/ SituaLons
Researchers’ knowledge
Birth of a category
Life-‐Cycle of a Category
Theory
Data Category
Intension Extension
Place in Conceptual hierarchy
Processes Contexts/ SituaLons
Researchers’ knowledge
Birth of a category
EvoluLon of a
category
Category
May cause change to exisLng theory
May lead to new understanding
Intension Extension Place in
Conceptual hierarchy
Conceptual change
New observaLons
Societal needs
Richer characterizaLon
Change
What knowledge are we missing !
How can we answer How and why aspect
of change ?
Change
We focus on
products of science and ignore
process of science
What knowledge are we missing !
How can we answer How and why aspect
of change ?
What’s in the process!
v Source of interpretaLon
v Can answer quesLons related to how and why aspect behind the change
Proposed Solution
Categories Process
of science
Now I understand why this category is the way it is…
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
give birth to
improve
modify connected as
ScienLfic ArLfacts
Process of Science
Conceptual Change
ApplicaLon
Database
Ontology
Workflow
Computational Framework
Categorical templates
Category-‐versioning system
Change event
Change Analyzer
• Recording changes and processes involved
• Analyze changes
• Broadcast changes
stub stub
Machine-‐learning techniques
• Neural networks • Bayesian Network …….
Service 1 Service 2 Service 3
Change event
Computational Framework
Categorical templates
Category-‐versioning system
Change event
Change Analyzer
• Recording changes and processes involved
• Analyze changes
• Broadcast changes
stub stub
Machine-‐learning techniques
• Neural networks • Bayesian Network …….
Service 1
Change event
Computational Framework
Categorical templates
Category-‐versioning system
Change event
Change Analyzer
• Recording changes and processes involved
• Analyze changes
• Broadcast changes
stub stub
Machine-‐learning techniques
• Neural networks • Bayesian Network …….
Service 1
Change event
Data-‐based
• Dataset • Training set • Classifier • Parameters • ValidaLon
method
Computational Framework
Categorical templates
Category-‐versioning system
Change event
Change Analyzer
• Recording changes and processes involved
• Analyze changes
• Broadcast changes
stub stub
Machine-‐learning techniques
• Neural networks • Bayesian Network …….
Service 1
Change event
Computational Framework
Categorical templates
Category-‐versioning system
Change event
Change Analyzer
• Recording changes and processes involved
• Analyze changes
• Broadcast changes
stub stub
Machine-‐learning techniques
• Neural networks • Bayesian Network …….
Service 2
Change event
Computational Framework
Categorical templates
Category-‐versioning system
Change event
Change Analyzer
• Recording changes and processes involved
• Analyze changes
• Broadcast changes
stub stub
Machine-‐learning techniques
• Neural networks • Bayesian Network …….
Service 3
Change event
Questions ?? Thanks to
Mark Gahegan (Supervisor) Gill Dobbie (co-‐supervisor) CeR Fellows
Prashant Gupta PhD student
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