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NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge
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NZ eResearch Symposium 2013 - Capturing the Flux in Scientific Knowledge

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

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

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  • 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  

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