Bojan Cestnik, Alenka Kern, Refining public sector services by applying innovative technologies


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#CeDEM13 Day 2 afternoon, Reflections, Main Hall, Chair: Morten Meyerhoff Nielsen

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Bojan Cestnik, Alenka Kern, Refining public sector services by applying innovative technologies

  1. 1. Refining public sector services byapplying innovative technologiesBojan Cestnik 1, 2Alenka Kern 31 Temida d.o.o., Ljubljana, Slovenia2 Jožef Stefan Institute, Ljubljana, Slovenia3 The Housing Fund of the Republic of Slovenia, Ljubljana, Slovenia
  2. 2. Talk outline—  Motivation and introduction—  Case studies◦  CeDEM 2011 papers topic ontology◦  Temporal focus shift of topics◦  Outlier documents detection – search for innovative leap ofideas—  Conclusions and further work
  3. 3. Motivation I—  Hypothesis: tools like–  text exploration,–  document clustering, and–  literature mining◦  have a potential to support the process of innovative problemsolving–  in e-government domain◦  by bridging information from different disciplines—  Source: E-Government Reference Library (EGRL)◦  4.674 peer-reviewed articles published in the last decade
  4. 4. Motivation II—  Creativity is a universal virtue—  Two kinds of creativity (G.A.Wiggins, 2012)◦  Spontaneous creativity (ideas appear spontaneous inconsciousness)–  e.g. Mozart: „When I am, as it were, completely myself, entirelyalone, and of good cheer – say traveling in a carriage, or walking aftera good meal, or during the night when I cannot sleep; it is on suchoccasions that my ideas flow best and most abundantly.“ (Holmes,2009, p. 315)◦  Creative reasoning–  The composer working to build a new version of a TV theme, onschedule and with constraints on „acceptable style“—  The computer software is the tool, the user is the creator—  Computational Creativity as an emerging field
  5. 5. Motivation III—  Increasing number of documents within all areas of humanexpertize—  Difficult to follow the progress even in a single specific area—  Innovative behavior is related to the comprehension of a particularfield
  6. 6. Literature mining—  Technologies:◦  Associative retrieval based on simple keywords◦  Inductive pattern search◦  Novelty: cross-context search incorporating scientific theoriesand models—  Knowledge discovery process involves:◦  Mining dynamic data streams◦  Incrementally updating existing models and theories—  Observation: vast majority of the mappings between scientificmodels and theories has been carried out exclusively by the humanscientists
  7. 7. Case studies—  First study: CeDEM 2011 topic ontology—  Source: E-Government library (Scholl, 2012)—  Second study: explore temporal focus shift, compare focus shifts oftitles and abstracts—  Third study: outlier documents detection – rare and worthwhilefor additional exploration since they might contribute to creativeleap of ideas
  8. 8. CeDEM 2011 papers
  9. 9. E-Government library (Scholl, 2012)Titles AbstractsPublication year Number % Number %2002 and before 515 11,0 281 10,32003-2004 706 15,1 479 17,52005-2006 813 17,4 331 12,12007-2008 938 20,1 500 18,32009 679 14,5 427 15,62010 637 13,6 420 15,32011 386 8,3 301 11,0Total 4.674 100,0 2.739 100,0
  10. 10. Focus shift through time – titles
  11. 11. Motivation IV—  Help experts in cross-domain discovery of new previously unknownrelations by supporting bisociative discovery—  Bisociation:◦  Term coined by Arthur Koestler,The act of creation, 1964◦  Bisociation is "any mental occurrence simultaneously associatedwith two habitually incomparable contexts“ - Koestler consideredit the essential mechanism of the creative process◦  The goal of FP7 EU Project BISON (Bisociation Networks forCreative Information Discovery): explore the concept ofbisociative discovery using graph-based data mining—  When we all think alike, no one thinks very much (A. Einstein)
  12. 12. Outlier documents detectioninnovative,technology, service,adopted, creativity,local, citizens,applications, diffusion,challengesmanagement, studies,discuss, relationship,effectiveness,presented, proposes,security, offer,frameworkE-Goverment librarydocumentssimilarity
  13. 13. Document clustering—  Outliers: good candidates to search for relations between concepts
  14. 14. An example of bisociationDr. Lawrence J. Fogel
  15. 15. Conclusions—  Presented case studies explore technological possibilities forsupporting creative processes in public sector—  Ontologies can be used for studying temporal focus shift within agiven domain—  Experts can use document clustering and similarity measures tosupport cross-domain discovery of new previously unknownrelations (bisociative discovery)