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Sexier, smarter, faster Information architecture with topic Maps

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Sexier, smarter, faster Information architecture with topic Maps

  1. 1. Sexier , faster , better information architecture through Topic Maps Saturday 30 th September, Alexander Johannesen DISCLAIMER : I love Topic Maps, and I get passionate about what I love. I think Topic Maps by itself will create peace on earth, solve world hunger and the energy crisis, so don’t expect me to dabble too much in the details.  Ask questions. Many of them.
  2. 2. “ Freedom without limits is just a word .”
  3. 3. <ul><li>(“ Feet of Clay ” by Terry Pratchett) </li></ul>“ Freedom without limits is just a word .” -- Dorfl the Golem
  4. 4. What do you know?
  5. 5. What do you know? <ul><li>Topic Maps </li></ul><ul><ul><li>The buzz, the rumour, the truth </li></ul></ul>
  6. 6. What do you know? <ul><li>Topic Maps </li></ul><ul><ul><li>The buzz, the rumour, the truth </li></ul></ul><ul><li>Semantic Data Modelling </li></ul><ul><ul><li>Information science, logic, math, data modelling </li></ul></ul>
  7. 7. What do you know? <ul><li>Topic Maps </li></ul><ul><ul><li>The buzz, the rumour, the truth </li></ul></ul><ul><li>Semantic Data Modelling </li></ul><ul><ul><li>Information science, logic, math, data modelling </li></ul></ul><ul><li>Linguistics and semiotics </li></ul><ul><ul><li>Language, semantics, script, signs, logos </li></ul></ul>
  8. 8. What do you know? <ul><li>Topic Maps </li></ul><ul><ul><li>The buzz, the rumour, the truth </li></ul></ul><ul><li>Semantic Data Modelling </li></ul><ul><ul><li>Information science, logic, math, data modelling </li></ul></ul><ul><li>Linguistics and semiotics </li></ul><ul><ul><li>Language, semantics, script, signs, logos </li></ul></ul><ul><li>Brainology </li></ul><ul><ul><li>Neuroscience, psychology, cognitive sciences </li></ul></ul>
  9. 9. Outline <ul><li>The brain, human cognition, patterns and models </li></ul><ul><li>Language, semiotics and linguistics </li></ul><ul><li>Life, universe and everything </li></ul><ul><li>Neuron, neural net, brain, human, family, friends, community, society, country, world, solar system, galaxy, super-galaxy, universe </li></ul><ul><li>Topic Maps, data models, ontologies </li></ul><ul><li>Information architecture </li></ul>
  10. 10. Women, fire and dangerous things <ul><li>Donna Maurer / Lakoff </li></ul><ul><li>'Miscellaneous' / 'everything else' categories are cognitively real, just not easy to use as navigation </li></ul><ul><li>Approximations in categorization are OK </li></ul><ul><li>Use prototypical items when communicating - they represent a category well </li></ul>
  11. 11. Models <ul><li>Models are concepts shaped by constraints </li></ul>“ freedom”
  12. 12. Models <ul><li>Models are concepts shaped by constraints </li></ul>“ freedom”
  13. 13. Models <ul><li>Models are concepts shaped by constraints </li></ul>“ freedom”
  14. 14. Mea culpa <ul><li>Alexander Johannesen, 34, Norwegian </li></ul><ul><li>Web Technology Manager for the National Library of Australia </li></ul>
  15. 15. Mea culpa <ul><li>Alexander Johannesen , 34 , Norwegian </li></ul><ul><li>Web Technology Manager for the National Library of Australia </li></ul>name age nationality workplace Job title
  16. 16. <ul><li>Alexander Johannesen, 34, Norwegian </li></ul><ul><li>Web Technology Manager for the National Library of Australia </li></ul><ul><li>IA, UCD, UX, Usability, Programming, Accessibility, Development, SOA, Web Services, Project Management, planning, scheming, Whinge, drink coffee </li></ul><ul><li>Married to an Aussie, got 2 girls aged 3 and 6, English Cocker Spaniel Oscar </li></ul><ul><li>Passionate, direct, politically incorrect, excitable </li></ul>Mea culpa
  17. 17. <ul><li>Alexander Johannesen, 34, Norwegian </li></ul><ul><li>Web Technology Manager for the National Library of Australia </li></ul><ul><li>IA, UCD, UX, Usability , Programming, Accessibility, Development, SOA, Web Services , Project Management, planning, scheming, Whinge, drink coffee </li></ul><ul><li>Married to an Aussie, got 2 girls aged 3 and 6, English Cocker Spaniel Oscar </li></ul><ul><li>Passionate , direct, politically incorrect , excitable </li></ul>Mea culpa status trait stance measurement Geographic origin privilege technology measurement nationality
  18. 18. What makes me excited <ul><li>Monteverdi and baroque music </li></ul><ul><li>My wife pretending to understand me </li></ul><ul><li>My kids learn something that I value highly </li></ul><ul><li>Seriously complex challenges </li></ul><ul><li>Enabling connotational knowledge </li></ul>
  19. 19. What makes me excited <ul><li>Monteverdi and baroque music </li></ul><ul><li>My wife pretending to understand me </li></ul><ul><li>My kids learn something that I value highly </li></ul><ul><li>Seriously complex challenges </li></ul><ul><li>Enabling connotational knowledge </li></ul>proponent Not classical music! partnership responsibility Has a price Measurement or Opinion context Information science
  20. 20. What makes me excited <ul><li>Monteverdi and baroque music </li></ul><ul><li>My wife pretending to understand me </li></ul><ul><li>My kids learn something that I value highly </li></ul><ul><li>Seriously complex challenges </li></ul><ul><li>Enabling connotational knowledge </li></ul>“ The self is a model of self-regulated constraints” Morals, ethical guidelines, memory, beliefs, wants, needs, joys, values …
  21. 21. Everything is a card sort
  22. 22. Everything is a card sort <ul><li>What is card sorting really? </li></ul>
  23. 23. Everything is a card sort <ul><li>What is card sorting really? </li></ul>
  24. 24. Everything is a card sort <ul><li>What is card sorting really? </li></ul>existentiality ordering manifestation being questioning
  25. 25. Everything is a card sort <ul><li>What is card sorting really? </li></ul>Sorting activity questioning Negative assurance / existentiality
  26. 26. Everything is a card sort <ul><li>What is card sorting really? </li></ul>Sorting activity questioning Negative assurance / existentiality constraints
  27. 27. Everything is a card sort <ul><li>What is card sorting really? </li></ul>Sorting activity questioning Negative assurance / existentiality Model Which is a pattern
  28. 28. <ul><li>What is card sorting really? </li></ul><ul><li>Classification of concepts </li></ul>Everything is a card sort
  29. 29. <ul><li>What is card sorting really? </li></ul><ul><li>Classification of concepts </li></ul>Everything is a card sort
  30. 30. <ul><li>What is card sorting really? </li></ul><ul><li>Classification of concepts </li></ul>Everything is a card sort
  31. 31. <ul><li>What is card sorting really? </li></ul><ul><li>Classification of concepts </li></ul><ul><li>The act of classification is to put constraints around concepts, </li></ul><ul><li>In order to create semantics through context </li></ul>Everything is a card sort
  32. 32. <ul><li>What is card sorting really? </li></ul><ul><li>Classification of concepts </li></ul><ul><li>The act of classification is to put constraints around concepts, </li></ul><ul><li>In order to create semantics through context </li></ul>Everything is a card sort
  33. 33. <ul><li>What is card sorting really? </li></ul><ul><li>Classification of concepts </li></ul><ul><li>The act of classification is to put constraints around concepts, </li></ul><ul><li>In order to create semantics through context </li></ul>Everything is a card sort “Tripe”
  34. 34. <ul><li>What is card sorting really? </li></ul><ul><li>Classification of concepts </li></ul><ul><li>The act of classification is to put constraints around concepts, </li></ul><ul><li>In order to create semantics through syntax </li></ul><ul><li>Constraints begets freedom </li></ul>Everything is a card sort
  35. 35. The fallacy of finding <ul><li>Mark Bernstein </li></ul><ul><li>Everything is contextual </li></ul><ul><li>You don’t have to start at the beginning </li></ul><ul><li>People will find their own meaning </li></ul><ul><li>Ontologies: structure for what not yet exists </li></ul><ul><li>We should create context, not manifestations </li></ul>
  36. 36. Summarium <ul><li>Everything is a concept; physical, metaphysical, abstract, silly, wacky, sensible </li></ul><ul><li>Concepts makes no sense without context </li></ul><ul><li>Models are the combination of concepts and constraints </li></ul><ul><li>Models are patterns </li></ul><ul><li>Models are our way of trying to make the complex real world comprehendible / manageable / simpler </li></ul><ul><li>Constraints create semantics / meaning </li></ul><ul><li>We match and merge external models with internal models; </li></ul><ul><ul><li>The more similar the model, the easier to merge </li></ul></ul><ul><ul><li>Merging largely different models takes repetition </li></ul></ul>
  37. 37. Data Modelling
  38. 38. Data model <ul><li>“ A data model is a model that describes in an abstract way how data is represented in a business organization, an information system or a database management system.” – WikiPedia </li></ul><ul><li>This bit goes here, that bit goes there, and this bit is the relationship between them … </li></ul><ul><li>It’s abstract </li></ul><ul><li>Simple, crude, barbaric, cut-down model </li></ul>
  39. 42. The basic problem with most data models are that they are …
  40. 43. … all different The basic problem with most data models are that they are …
  41. 44. … all different, requires special knowledge about the model The basic problem with most data models are that they are …
  42. 45. … all different, requires special knowledge about the model, often locked into the environment The basic problem with most data models are that they are …
  43. 46. … all different, requires special knowledge about the model, often locked into the environment, vary in complexity and can be a bugger to understand and comprehend The basic problem with most data models are that they are …
  44. 47. … all different, requires special knowledge about the model, often locked into the environment, vary in complexity and can be a bugger to understand and comprehend, requires all new Knowledge every time a data model is created The basic problem with most data models are that they are …
  45. 48. … all different, requires special knowledge about the model, often locked into the environment, vary in complexity and can be a bugger to understand and comprehend, requires all new Knowledge every time a data model is created, gives a specific Interpretation of requirements … The basic problem with most data models are that they are …
  46. 49. … all different, requires special knowledge about the model, often locked into the environment, vary in complexity and can be a bugger to understand and comprehend, requires all new Knowledge every time a data model is created, gives a specific Interpretation of requirements … which suggests especially one thing
  47. 50. The metamodel <ul><li>A general data model </li></ul><ul><li>A metamodel in which more complex models can be created </li></ul><ul><li>A framework that can encapsulate and reuse sub-models </li></ul>
  48. 51. Wait for it …
  49. 52. Topic Maps
  50. 53. An open ISO * standard from 2000/2001/2005/2006 * International Standards Organisation
  51. 54. Bits and chunks <ul><ul><li>Reference model (TMRM) </li></ul></ul><ul><ul><li>Data model (TMDM) </li></ul></ul><ul><ul><li>XML (and non-standard notation) exchange format (XTM) </li></ul></ul><ul><ul><li>Query language (TMQL) </li></ul></ul><ul><ul><li>Constraints language (TMCL) </li></ul></ul>
  52. 55. What good does it do? <ul><li>Makes you ontology focused </li></ul><ul><ul><li>Semantics </li></ul></ul><ul><ul><li>Models </li></ul></ul><ul><ul><li>What Mark Bernstein said : “ structure for what not yet exists ” and “ Everything is contextual ” </li></ul></ul><ul><li>Real reuse value </li></ul><ul><li>Realising any structure </li></ul>
  53. 56. In short <ul><li>Came out of the information science domain as an answer to research in cognitive sciences </li></ul><ul><li>Topic Maps is a meta technology standard (you need to fill out the blanks) </li></ul><ul><li>Enables both ontologies and data (unlike RDF) </li></ul><ul><li>Enables sharing and merging of models </li></ul>
  54. 57. Topics or concepts or subjects
  55. 59. point
  56. 60. concept
  57. 61. node
  58. 62. tag
  59. 63. page
  60. 64. topic The topic Topics
  61. 65. topics
  62. 66. <topic id=“346534” />
  63. 67. “ NLA” “ Alex” “ Topic Maps” “ IA” <topic id=“nla”> <name>NLA</name> </topic>
  64. 68. “ NLA” (institution:library) “ Alex” (being:person) “ Topic Maps” (concept) “ IA” (practice:information-structuring) <topic id=“nla” type=“library”> <name>NLA</name> </topic>
  65. 69. “ NLA” (institution:library) (library:reference) (library:academic) “ Alex” (being:person) (homo-sapien-sapiens) (profession:IT:development) “ Topic Maps” (concept) (standards:iso:tm_1.1) (info-science:knowledge-representation) “ IA” (practice:information-structuring) (concept:interface:HCI) (info-science:structuring)
  66. 70. <topic id=“nla” type=“library”> <name>NLA</name> </topic> “ NLA” (institution:library) “ Alex” (being:person) “ Topic Maps” (concept) “ IA” (practice:information-structuring)
  67. 71. “ Library” <topic id=“nla” type=“library”> <name>NLA</name> </topic> <topic id=“ont-l23”> <name>Library</name> </topic> “ NLA” (institution:library) “ Alex” (being:person) “ Topic Maps” (concept) “ IA” (practice:information-structuring)
  68. 72. “ Library” <topic id=“nla” type=“library”> <name>NLA</name> </topic> <topic id=“ont-l23”> <name>Library</name> </topic> “ NLA” (institution:library) “ Alex” (being:person) “ Topic Maps” (concept) “ IA” (practice:information-structuring)
  69. 73. Associations And constraints
  70. 74. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) <association> <member id=“alex” /> <member id=“nla” /> </association> “ IA” (practice:information-structuring)
  71. 75. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Works for Employs <association> <member id=“alex” roletype=“works-for” /> <member id=“nla” roletype=“employs” /> </association> “ IA” (practice:information-structuring)
  72. 76. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Works for Employs Employment <association type=“employment”> <member id=“alex” roletype=“works-for” /> <member id=“nla” roletype=“employs” /> </association> “ IA” (practice:information-structuring)
  73. 77. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) “ Donald” (person) “ Daisy” (person) “ Goofy” (person) Associations : many to many “ IA” (practice:information-structuring)
  74. 78. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Works for Employs Employment “ IA” (practice:information-structuring)
  75. 79. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Interesting Interested Interest “ IA” (practice:information-structuring)
  76. 80. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Practicioned Practice Practician “ IA” (practice:information-structuring)
  77. 81. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Practice Technology Associations : Applied knowledge Applied knowledge “ IA” (practice:information-structuring)
  78. 82. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Applied knowledge Giver Associations : Applied knowledge “ IA” (practice:information-structuring) Practice Technology
  79. 83. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Applied knowledge Giver Target Associations : Applied knowledge “ IA” (practice:information-structuring) Practice Technology
  80. 84. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Giver Target Associations : multiple roles Applied knowledge <association type=“applied-knowledge”> <member id=“alex” roletype=“giver” /> <member id=“nla” roletype=“target” /> <member id=“tmseminar” roletype=“medium” /> <member id=“tm” roletype=“concept” /> </association> “ IA” (practice:information-structuring) Practice Technology
  81. 85. Occurrences
  82. 86. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Occurrences “ IA” (practice:information-structuring)
  83. 87. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Occurrences <topic id=“nla” type=“library”> <name>NLA</name> </topic> “ IA” (practice:information-structuring)
  84. 88. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Occurrences http://www.nla.gov.au (website) <topic id=“nla” type=“library”> <name>NLA</name> <occurrence type=“website” href=“ www.nla.gov.au ” /> </topic> “ IA” (practice:information-structuring)
  85. 89. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Occurrences 3495x34874 (map-coordinates) <topic id=“nla” type=“library”> <name>NLA</name> <occurrence type=“map-coordinates”> 3495x34874 </occurrence> </topic> “ IA” (practice:information-structuring) http://www.nla.gov.au
  86. 90. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Occurrences 3495x34874 www.nla.gov.au/map.gif (map) <topic id=“nla” type=“library”> <name>NLA</name> <occurrence type=“map” href=“ www.nla.gov.au /map.gif” /> </topic> “ IA” (practice:information-structuring) http://www.nla.gov.au
  87. 91. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Occurrences http://iainstitute.org/ (website) “ IA” (practice:information-structuring)
  88. 92. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Occurrences http://iainstitute.org/ (explanation:short) “ IA” (practice:information-structuring) practice of structuring information
  89. 93. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Occurrences http://iainstitute.org/ practice of structuring information Shelter.nu/schema/ia_1.0 Associations (schema) “ IA” (practice:information-structuring)
  90. 94. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Associations “ IA” (practice:information-structuring)
  91. 95. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Associations : Applied knowledge “ IA” (practice:information-structuring)
  92. 96. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Associations “ IA” (practice:information-structuring)
  93. 97. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Associations “ IA” (practice:information-structuring)
  94. 98. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) A Topic Map Ontology “ IA” (practice:information-structuring)
  95. 99. T opics A ssociations O ccurrences
  96. 100. Ontology
  97. 101. Ontology <ul><li>Is a data model (!!) </li></ul><ul><li>The “stuff” that surrounds your data </li></ul><ul><li>Domain definition </li></ul><ul><li>Bits of metadata to tell a story </li></ul><ul><li>Separation of model and metadata </li></ul><ul><li>Even if you’re not using Topic Maps tools, thinking in ontological terms helps you explore your basic-level categories and finding goals in your information. </li></ul>
  98. 102. “ NLA” (library) “ Alex” (person) “ Topic Maps” (concept) Ontology “ IA” (practice:information-structuring)
  99. 103. “ Alex” (person) “ Topic Maps” (concept) Ontology “ NLA” (library) “ IA” (practice:information-structuring)
  100. 104. - library - person - concept - seminar Ontology : taxonomy - organisation - resources - training - books - maps - Physical objects - goals - needs - funds - goodwill - objects - methods
  101. 105. Ontology : controlled vocabulary Organisation : corporation (see also) , enterprise (narrower term) Person : human (broader term) , worker (narrower term) Resource : person (narrower term, see also) , book (narrower term) , map (narrower term) Topic Maps : concept (broader term) Training : seminar (narrower term) , course (narrower term) , presentation (narrower term, see also) Computer : server (narrower term, see also) , resource (broader term) Collection : library (broader term) , set (narrower term) All-together-now example
  102. 106. Ontology : controlled vocabulary Migrants USE Immigrants Military historians (LCSH) BT Historians Military spouses (local) BT Spouses RT War widows Millers (LCSH) UF Flour millers Milliners USE Hatters and milliners Mine owners (local) UF Mine proprietors Mine proprietors USE Mine owners Mineralogists (LCSH) BT Earth scientists OCCUPATIONS THESAURUS Recommended for contributions to the Australian Register of Archives and Manuscripts
  103. 107. Ontology : controlled vocabulary Migrants USE Immigrants Military historians (LCSH) BT Historians Military spouses (local) BT Spouses RT War widows Millers (LCSH) UF Flour millers Milliners USE Hatters and milliners Mine owners (local) UF Mine proprietors Mine proprietors USE Mine owners Mineralogists (LCSH) BT Earth scientists OCCUPATIONS THESAURUS Recommended for contributions to the Australian Register of Archives and Manuscripts “ USE” #ontology-term-use
  104. 108. Ontology : controlled vocabulary Migrants USE Immigrants Military historians (LCSH) BT Historians Military spouses (local) BT Spouses RT War widows Millers (LCSH) UF Flour millers Milliners USE Hatters and milliners Mine owners (local) UF Mine proprietors Mine proprietors USE Mine owners Mineralogists (LCSH) BT Earth scientists OCCUPATIONS THESAURUS Recommended for contributions to the Australian Register of Archives and Manuscripts “ USE” #ontology-term-use
  105. 109. Ontology : controlled vocabulary Migrants USE Immigrants Military historians (LCSH) BT Historians Military spouses (local) BT Spouses RT War widows Millers (LCSH) UF Flour millers Milliners USE Hatters and milliners Mine owners (local) UF Mine proprietors Mine proprietors USE Mine owners Mineralogists (LCSH) BT Earth scientists OCCUPATIONS THESAURUS Recommended for contributions to the Australian Register of Archives and Manuscripts “ USE” #ontology-term-use
  106. 110. Ontology : controlled vocabulary Migrants USE Immigrants Military historians (LCSH) BT Historians Military spouses (local) BT Spouses RT War widows Millers (LCSH) UF Flour millers Milliners USE Hatters and milliners Mine owners (local) UF Mine proprietors Mine proprietors USE Mine owners Mineralogists (LCSH) BT Earth scientists “ USE” OCCUPATIONS THESAURUS Recommended for contributions to the Australian Register of Archives and Manuscripts “ Milliners” (occupation) “ Hatters and milliners” (occupation) #ontology-term-use
  107. 111. Ontology : controlled vocabulary Migrants USE Immigrants Military historians (LCSH) BT Historians Military spouses (local) BT Spouses RT War widows Millers (LCSH) UF Flour millers Milliners USE Hatters and milliners Mine owners (local) UF Mine proprietors Mine proprietors USE Mine owners Mineralogists (LCSH) BT Earth scientists “ USE” OCCUPATIONS THESAURUS Recommended for contributions to the Australian Register of Archives and Manuscripts “ Milliners” (occupation) “ Hatters and milliners” (occupation) Non-preferred #ontology-term-use
  108. 112. Ontology : controlled vocabulary Migrants USE Immigrants Military historians (LCSH) BT Historians Military spouses (local) BT Spouses RT War widows Millers (LCSH) UF Flour millers Milliners USE Hatters and milliners Mine owners (local) UF Mine proprietors Mine proprietors USE Mine owners Mineralogists (LCSH) BT Earth scientists “ USE” OCCUPATIONS THESAURUS Recommended for contributions to the Australian Register of Archives and Manuscripts “ Milliners” (occupation) “ Hatters and milliners” (occupation) Preferred Non-preferred #ontology-term-use A small digression
  109. 113. Ontology : controlled vocabulary Migrants USE Immigrants Military historians (LCSH) BT Historians Military spouses (local) BT Spouses RT War widows Millers (LCSH) UF Flour millers Milliners USE Hatters and milliners Mine owners (local) UF Mine proprietors Mine proprietors USE Mine owners Mineralogists (LCSH) BT Earth scientists “ USE” OCCUPATIONS THESAURUS Recommended for contributions to the Australian Register of Archives and Manuscripts Preferred Non-preferred #ontology-term-use “ Preferred” #nla-ontology-term-use-preferred
  110. 114. Ontology : controlled vocabulary OCCUPATIONS THESAURUS Recommended for contributions to the Australian Register of Archives and Manuscripts “ Milliners” (occupation) “ Hatters and milliners” (occupation) Preferred Non-preferred Milliners What is your occupation? Your occupation is better known as “ Hatters and milliners” “ USE” #ontology-term-use
  111. 115. Ontology : controlled vocabulary : it’s all about types OCCUPATIONS THESAURUS Recommended for contributions to the Australian Register of Archives and Manuscripts ontology
  112. 116. Persistent identifiers and and merging
  113. 117. PSI XY project My new app Fish ontology
  114. 118. PSI “ Alex” #nla-resource-person #nla-person-ajohanne “ Alexander” #nla-resource-team-member #nla-person-ajohanne “ Johannesen, Alex” #nla-resource-employee #nla-person-ajohanne XY project My new app Fish ontology
  115. 119. PSI “ Alex” #nla-resource-person #nla-person-ajohanne “ Alexander” #nla-resource-team-member #nla-person-ajohanne “ Johannesen, Alex” #nla-resource-employee #nla-person-ajohanne XY project My new app Fish ontology
  116. 120. PSI “ Alex ” , “ Alexander ” , “ Johannesen, Alex ” #nla-resource-team-member #nla-resource-person #nla-resource-employee #nla-person-ajohanne “ Alex” #nla-resource-person #nla-person-ajohanne “ Alexander” #nla-resource-team-member #nla-person-ajohanne “ Johannesen, Alex” #nla-resource-employee #nla-person-ajohanne XY project My new app Fish ontology
  117. 121. ontology One data model is easy to share, as is the language and terms used XY project My new app Fish ontology
  118. 122. Quick prototyping and reuse of data by merging ontology Fiddle Project XY project My new app Fish ontology
  119. 123. ontology Fiddle Project Expansions and extensions to scope and data stores made easy Dingbat Project XY project My new app Fish ontology
  120. 124. All encompassing applications are possible ontology Fiddle Project Dingbat Project XY project My new app Fish ontology
  121. 125. Share your structures and data with outside sources as well ontology Fiddle Project Dingbat Project AustLit project XY project My new app Fish ontology
  122. 126. Demo Opera! (This will make Thomas go Pivot crazy!!)
  123. 127. What do I do Information architecture
  124. 128. What do I do Information architecture <ul><li>Big data sets </li></ul><ul><ul><li>Between 10.000 – 50.000 pages </li></ul></ul><ul><ul><li>Min. 10 million objects </li></ul></ul><ul><li>Balancing crazy vs. statistics </li></ul>
  125. 129. What do I do Information architecture <ul><li>Big data sets </li></ul><ul><li>Between 1000 pages - 20.000 pages </li></ul><ul><li>Between 1 million - 10 million objects </li></ul><ul><li>Why do I use Topic Maps for this? </li></ul><ul><li>Speaks my language (half-techno / half-human) </li></ul><ul><li>It humanizes metadata </li></ul><ul><li>Fantastic reusability </li></ul><ul><li>Balancing crazy (opinions) vs. statistics </li></ul><ul><li>Semantically queriable </li></ul>
  126. 130. <ul><li>Home | index | NLA,menu:banner </li></ul><ul><li>Find | index,find | menu:main </li></ul><ul><li>Books | search,find | books </li></ul><ul><li>Ephemera | search,find | books,epherma </li></ul><ul><li>Microform | search,find | books,microform </li></ul><ul><li>Rare Books | search,find | books,rare_books </li></ul><ul><li>Journals | search,find | journals </li></ul><ul><li>Newspapers | search,find | newspapers </li></ul><ul><li>Dance | search,find | dance </li></ul><ul><li>Guides | search,find | guides,menu:banner </li></ul><ul><li>Electronic | search,find | electronic </li></ul><ul><li>Film and Video | search,find | multimedia </li></ul><ul><li>Manuscripts | search,find | manuscripts </li></ul><ul><li>Finding Aids Overview | search,find | manuscripts,finding_aids </li></ul><ul><li>About Finding Aids | search,find | manuscripts,finding_aids </li></ul><ul><li>Alphabetical List | index,find | manuscripts </li></ul><ul><li>Collection Numbers | index,find | manuscripts </li></ul><ul><li>Subject Guides | index,find | manuscripts </li></ul><ul><li>Maps | search,find | maps </li></ul><ul><li>Music | search,find | music </li></ul><ul><li>Symphony Australia Collection Listing : Scope | intro,find | music </li></ul><ul><li>Symphony Australia Collection Listing : Composer order | index,find | music </li></ul><ul><li>Symphony Australia Collection Listing : Title order | index,find | music </li></ul><ul><li>Symphony Australia Collection Listing : Alfred Hill listing | index,find | music,alfred_hill </li></ul><ul><li>Oral History | search,find | oral_history </li></ul><ul><li>Performing Arts | search,find | performing_arts </li></ul><ul><li>Pictures | search,find | pictures </li></ul><ul><li>Lists | index,find | pictures </li></ul><ul><li>Asian | search,find | asia,geography </li></ul><ul><li>Burmese | search,find | asia,burma,geography </li></ul><ul><li>Cambodian | search,find | asia,cambodia,geography </li></ul>
  127. 131. types tags
  128. 132. Mea Culpa!
  129. 133. The end

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