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Ontology dojo presentation eia 18 workshop take away

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Workshop presentation given at European Information Architecture Summit 2018.

Published in: Design

Ontology dojo presentation eia 18 workshop take away

  1. 1. Ontology Dojo Using an Ontological Mindset to Solve IA Problems Name: Height: Weight: Age:
  2. 2. Hello…Metadata Please • Last IA/Industry Book Read • Favorite Ice Cream flavor/Dessert • Favorite city to travel to
  3. 3. Ben reads bookthe Grammatical Structure Matters Bens lasīja grāmatu Heluheluʻo Ben i ka puk About Language
  4. 4. Ontology and the Development Life Cycle Need ProductRequirements Design/ Architecture Engineering Development Test/ Validation House • 3 Bedroom • 2 Bath • Home Office • 1 Story Inspection
  5. 5. Ontology and the Development Life Cycle Need ProductRequirements Design/ Architecture Engineering Development Test/ Validation Wine Website Inspection • Wine Varieties • Find Information • Common language to exchange information • XML • RDF • OWL • NEO4j • SPARQL Concept Map Entity Relationship Diagram
  6. 6. What is Ontology? • Philosophical study of being and reality • “Ontology” has taken on two meanings: • Classical Ontology (a study of philosophy focused on the nature of being) • Modern Applied Ontology (Using ontological theory to describe things to facilitate the design of information systems) Ontology is a deliberate method of characterization
  7. 7. Who Uses Ontology? Information Architects Librarians/ Archivists/ Taxonomists Philosophers Investigators/ Law Enforcement Geographers
  8. 8. History of Ontology Parmenides Plato & Tom GruberJean Paul Sartre Study of Existence Concrete vs Abstract Applied
  9. 9. From Classical to Modern Applied Ontology • Describe things and their relationships to other things • Model and document things • Facilitate finding things and sharing information Prove and describe existence of things. Classical Modern
  10. 10. Modern Ontologies • Highest Level • “Metaontology” • Rosetta Stone Upper Level • Scoped by a topic • “Unique” Language (Controlled Vocabulary)Domain • Mix of Upper Level and Domain • Usually very technicalHybrid
  11. 11. Modern Ontologies Dogs Cats Fish Pets Biology Document Domain Ontologies Upper Level Ontologies
  12. 12. The Building Blocks of Ontology Objects Predicates Attributes Name: Height: Weight: Age: Domains Class
  13. 13. Limits must be placed on what is examined “The ontology of…” Domains
  14. 14. Ontology IA/UX Comp Sci Business Library Science BOLO Domain Scope Domain Scope • Subject • Topic • Verticals • Stove Pipes Domains
  15. 15. Broad Narrow Build Borrow USAGE TOPIC SCOPE ANTIDEPRESSANTS WALMART INVENTORY Healthcare Finance RHONE WINES Domains Upper Domain
  16. 16. Domains The purpose of _________________________is to ____________________________ so that___________________________
  17. 17. Domains The purpose of the ______________Ontology is to ____________________________ so that___________________________ Michigan Wine Show the relationships between wines varieties users can discover the wine varieties they like
  18. 18. Domains Narrow or Broad? • Cruise Ship Entertainment • Gardening Techniques • Dog Breeds
  19. 19. Objects The difference between nothing and being
  20. 20. Ontology IA/UX Comp Sci Business Library Science BOLO • Being • Object • Object • Artifact • Object • Entity • Vertex Entity Object Artifact • Object • Artifact • Nouns • Product • Things • Concepts Objects
  21. 21. • Interactions • Organization • Life Cycle • Ownership • Identification • Attributes • Concrete • Abstract Type Description RelationshipsGovernance Objects
  22. 22. Concrete • Does not have to be something you “touch” • Concrete objects can take action • Adheres to the laws of physics • Much easier to communicate and describe to others Objects
  23. 23. Abstract • May have to “invent” physics • Harder to describe due to perspective • May not be able to take action • Often reuse terms and concepts we are familiar with. Objects
  24. 24. On the edges • Virtual objects can be hard to categorize • May be a matter of perspective • Interaction between abstract and concrete objects can be interesting Citizens United v. FEC (2010) Objects
  25. 25. Objects Concrete or Abstract? • The Empire State Building • 2021 EIA Summit • YouTube (The website)
  26. 26. The actions among things Predicates
  27. 27. Ontology IA/UX Comp Sci Business Library Science BOLO • Predicate • Link • Relationship • Link • Predicate • Edge • Relationship • Link • Relationship • Link • Rule • Constraint • Action Predicates
  28. 28. Ben reads bookthe Action of one object on another Predicates
  29. 29. Ben reads book the Action of one object on another Predicates
  30. 30. HyponymHypernym MeronymHolonym Synonym Antinym General Specific Whole Part Like Different Predicates
  31. 31. IS A TYPE OF Animal Invertebrate Insect Crustacean Vertebrate Mammal Reptile Uses • Taxonomies • Card Sorting • Affinity Diagrams • Concept Map Hypernym Hyponym Predicates
  32. 32. IS PART OF Uses • Site Map • Content Inventory • Venn Diagram • Card Sort • Concept Map • Affinity Diagram Meronym Meronym Meronym Holonym Predicates
  33. 33. IS PART OF Example Part Type (1) The handle is part of the mug. Physical (2) The remote control is part of the stereo system. Collection (3) The left half is your part of the cake. Portion (4) The cutlery is part of the tableware. Set (5) The contents of this bag is only part of what I bought. Unrelated Collection (6) That area is part of the living room. Spatial (7) The outermost points are part of the perimeter. Location (8) The first act was the best part of the play. Temporal Different Types of Parts Predicates
  34. 34. Synonym/Antinym IS LIKE Uses • Thesaurus • Controlled Vocabulary • Card Sort • Concept Map • Affinity Diagram
  35. 35. TAXONOMIES CONCEPT MAPS SITE MAPS AFFINITY MAPPING/ CARD SORTS Look for the lines Predicates Predicates are Everywhere
  36. 36. Ben reads book the buys owns Predicates Other Predicates the the
  37. 37. • Try to use predefined/ established predicates • May have to create abstract objects • Make a legend/directory of your predicates Is a Type of Is a Part of Is Like Is not Like Is a Type of Is a Part of Is Like Is not Like Purchases Reviews Creates Predicates Other Predicates
  38. 38. • Try to use predefined/ established predicates • May have to create abstract objects • Make a legend/directory of your predicates Tammy Pez Friend of Predicates Other Predicates
  39. 39. • Try to use predefined/ established predicates • May have to create abstract objects • Make a legend/directory of your predicates Tammy PezTammy Pez Friend- ship Predicates Other Predicates
  40. 40. Predicates Employee Department ?
  41. 41. Predicates LEGO Toy ?
  42. 42. Predicates Start Time Event ?
  43. 43. Predicates Driver School Bus ?
  44. 44. Name: Height: Weight: Age: “Metadata liberates us, liberates knowledge.” David Weinberger Attributes
  45. 45. Ontology IA/UX Comp Sci Business Library Science BOLO Attributes • Attributes • Metadata • Attributes • Properties • Metadata • Metadata • Properties • Metadata • Characteristics • Descriptor • Descriptive data • Associative data • Adjectives/adverbs Name: Height: Weight: Age: Attributes
  46. 46. • Captures characteristics • Almost endless data points can be captured…know the scope of your project • Attributes/Metadata has evolved tremendously over past 20 years Name: Height: Weight: Age: Make: MG Model: TD Midget Body Style: Convertible Color: Lt Brown Era: Vintage Make: MG Model: TD Midget Height: 53” Length: 145” Width: 59” Make: MG Model: TD Midget Website: www.mg.co.uk Description: The MG T series is a range of body-on- frame convertible sports cars that were… Video: Attributes
  47. 47. Name: Height: Weight: Age: UNIQUE IDENTIFIERS ID EVERYTHINGObjects Predicates Classes Metadata Deliverables Notes Clients Attributes
  48. 48. Name: Height: Weight: Age: UNIQUE IDENTIFIERS Prefix Object Type OBJ General Object PSN Persona TAX Taxonomy PRD Predicate OBJ-38 TAX-04 Attributes
  49. 49. Attributes Name: Height: Weight: Age: Book in a Library Title Author Edition ISBN Price Subject
  50. 50. Ontology IA/UX Comp Sci Business Library Science BOLO Class • Class • Group • Collection • Category • Class • Group • Collection • Class • Group • Collection • Category • Class • Subject • Schema • Taxon • Type Classes
  51. 51. • When several object share one or more non- identifying attributes, they may makeup a class • Sales Manager is the class in the example • The more attributes that match, the more specific the class (North American Sales Manager NAME: Alan Moore OCCUPATION: Sales Manager LOCATION: North America SALARY: $88,000 NAME: Sara Whatley OCCUPATION: Sales Manager LOCATION: South America SALARY: $93,000 NAME: Torey Joliet OCCUPATION: Sales Manager LOCATION: North America SALARY: $82,000 Classes
  52. 52. “To be or not to be” “I think therefore I am” “The definition of ‘is,’ is” Class/Predicate/Attribute Pitfalls
  53. 53. Mammal Is a type of A Cat Maria Mammal Is a type ofClass Instance Is a Class/Predicate/Attribute Pitfalls
  54. 54. Product Clothes Pants Shirts North Face Solar Plaid Tools Hand Hammer Craftsman Power Impact Power Class/Predicate/Attribute Pitfalls
  55. 55. Organization Corporate Offices Divisions Branches HR Legal Finance Sections Teams Offices Field Offices Franchises Class/Predicate/Attribute Pitfalls
  56. 56. Application to IA
  57. 57. Is a Part ofIs a Type of HIERARCHIES (TAXONOMIES, SITE MAPS, AFFINITY DIAGRAMS) Object Object Object Object Object ObjectObject Object Predicates (Is a Type of) (Is a Part of) • Try to use one predicate type per diagram • Mix predicates vertically Deliverables
  58. 58. PERSONAS (ORGANAS, SPEC SHEETS….) SusanThe Administrator Location: Apps: iControl NetView HRBase Basic Intranet Admin Stats ~~~: xxxxxxx ~~~: xxxx ~~~: xxxxx Description: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam suscipit lacus sit amet eros vehicula sagittis. In in sapien turpis. Donec risus nisi, bibendum id tristique non, rutrum vitae. Availability: Content Knowledge: Training: Investment: P-12.AB20 Deliverables
  59. 59. SusanThe Administrator Location: Apps: iControl NetView HRBase Basic Intranet Admin Stats ~~~: xxxxxxx ~~~: xxxx ~~~: xxxxx Description: Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam suscipit lacus sit amet eros vehicula sagittis. In in sapien turpis. Donec risus nisi, bibendum id tristique non, rutrum vitae. Availability: Content Knowledge: Training: Investment: P-12.AB20 CLASS GLOBAL METADATA CLASS METADATA ID PERSONAS (ORGANAS, SPEC SHEETS….) Deliverables
  60. 60. CONCEPT MAPS Title (Domain) Is a Part of Is a Type of Uses Object Object Object Object Object Predicate Predicate Predicate Predicate Predicate Attributes Attributes Attributes Attributes Attributes Class Deliverables
  61. 61. Is a Part of Is a Type of Uses System ToolsAdministrator Anchorage Office iControl XYZ Corp’s Intranet HRBase NetView Personnel (Persona) (Taxonomy) (UI Wireframe) P-12.AB20 CONCEPT MAPS—TYING IT ALL TOGETHER Deliverables
  62. 62. Object/Predicate UX/UI Objects UIs Predicate Experience
  63. 63. Object/Predicate UX/UI Tammy PezTammy Pez Friend- ship Profile UI Friendship UI Experience Interactions
  64. 64. Low Fidelity • Initial Design • Rapid Design • Presentation Sketching Tools Sticky Notes/Paper Listing Tools Tools and Technologies
  65. 65. High Fidelity • Capture Metadata/Predicates/Classes • Actionable ( Development Ready) Designs • Record Keeping Mind Mapping CAD Tools and Technologies
  66. 66. • Extensible Markup Language (XML) • Unified Modeling Language (UML) • Ontological Web Language (OWL) • Resource Description Framework (RDF) Tools and Technologies
  67. 67. Goal: Complete a series of activities that simulate ontological thinking and the creation of an ontology to solve a complex IA problem. Problem we are solving: We are building a website or application for a diverse group of conference visitors to Dublin in 2018, and need to make a wide variety of transportation options, and their interplay, clear Ontological Thinking Activity
  68. 68. DUBLIN
  69. 69. TRANSIT
  70. 70. Mapping out a model of the complex system will help us understand how to best situate and plan our website within the already complex Dublin Transit ecosystem. Mapping out this ecosystem will also allow us to have a shared vocabulary around stakeholders, users, inputs, outputs, integrations, and relationships between these entities, leading to more clear shared communication on project goals and complexities. Ecosystem Model
  71. 71. Ecosystem Example: Duke University Press
  72. 72. STEP 1: List out all possible objects
  73. 73. Objects are nodes in a complex system. Think of any noun you might use when talking about transit in Dublin. They can be: • People / Roles • Groups • Organizations & Agencies • Places • Artifacts • Payment types Pro-Tip: group all like items on the same color Post – it – ie: all people are blue, all systems green. Objects Rail Car Sharing Organizer Attendee Aircoach Taxi Bus Luas Trains Leap Card Permit MyTaxi
  74. 74. • Looking at your Wikipedia articles, pull out: • Objects (nouns) • Classes (categories you need to call out) • If time - Attributes (Adjectives) • Generate other objects you know to be true but are not in the Wikipedia article (see the article co-chairs put together, personal experience) • Write on your post its – remember, ideally each class should have it’s own color • Ren & Jess walk around and answer questions • 15 minutes to do this Finding your Objects
  75. 75. STEP 2: Map out Objects on the wall
  76. 76. STEP 3: Think about connections (AKA: Predicates)
  77. 77. Predicates are typically verbs and prepositions that show relationships between objects. Examples include: • Is a part of • Is a type of • Is like • Serves • Purchases Remember: Keep your predicates streamlined, uniform, and minimal. Predicates
  78. 78. STEP 4: Move Objects around before adding Predicates
  79. 79. STEP 5: Sketch in Predicates
  80. 80. It’s ok for your Predicates to be messy at this stage
  81. 81. Group sharing and conversation: 1. Describe your progress & approach 2. Challenges 3. Open Questions 4. Reflections Sharing
  82. 82. Ready to go out and CLASS First Round of Ontology Dojo
  83. 83. • What do you do with it once you're done? • Solving the initial ask… how does this help us actually make the website now? • Deriving sitemaps, taxonomies, user journeys from this ontology • How could you start to do some organization with this? Sitemaps? Would different table have different sitemaps? Why? • Methods of output and application • XML vs. UML vs. OWL vs. RDF - would be nice to see a chart comparing these • Tools of the Trade • CMAP • Excel • Other Software -(ie: Visio, Omnigraffle) • Taxobank Now What?
  84. 84. • Books, Blogs, Training, Events • Carrie Hane & Mike Atherton: Designing Connected Content • Carrie Hane – Connected Content @IAS 2018 • Kat King - Personal Ontology Maps @IAS 2018 • Ontology Worksheet • https://ubwp.buffalo.edu/ncor/training-and-education/ • Stanford on Parthood • Ontology • Upper Ontology • How to follow up with questions • Ren: @info_do • Jess: jess@rs21.io Additional Resources
  85. 85. Sources • http://www.imdb.com/title/tt0087538/?ref_=ttmd_md_nm • https://upload.wikimedia.org/wikipedia/commons/e/ed/Parmenides.jpg • https://upload.wikimedia.org/wikipedia/commons/4/4a/Plato-raphael.jpg • https://www.flickr.com/photos/government_press_office/6470403371/ • https://upload.wikimedia.org/wikipedia/commons/6/6e/Golde33443.jpg • https://upload.wikimedia.org/wikipedia/commons/2/24/George_Washingt on_Bridge_from_New_Jersey-edit.jpg
  86. 86. • http://library.corporate- ir.net/library/17/176/176060/mediaitems/93/a.com_logo_RGB.jpg • https://commons.wikimedia.org/wiki/File:Title_page_William_Shakes peare%27s_First_Folio_1623.jpg • https://commons.wikimedia.org/wiki/File:Frans_Hals_- _Portret_van_Ren%C3%A9_Descartes.jpg • https://commons.wikimedia.org/wiki/File:44_Bill_Clinton_3x4.jpg • http://www.visualthesaurus.com/ • http://cmap.ihmc.us/docs/theory-of-concept-maps

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