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VERNACULAR CLASSIFICATION: HUMANITIES
NETWORKED INFRASTRUCTURE (HUNI)
Department of Digital HumanitiesToby Burrows
HuNI (Humanities Networked Infrastructure)
•  Aggregates data from 30 different Australian humanities
datasets
•  Data are...
Challenges for HuNI
•  How to organize and link heterogeneous data for
browsing – without entirely pre-determining the
str...
Concept
HuNI Record Category
Event Organisation Person Place Work
More icons = mo
PERSON A natural person
ORGANISATION A c...
HuNI: creating collections
•  Users are able to create their own collections of data
•  They can create categories and cla...
HuNI: socially-linked data
•  Users are also able to create links between entities
•  These links are public, by default
•...
HuNI: classification and categorization 1
•  Specific individual entities and phenomena are the focus of the HuNI
data agg...
HuNI: classification and categorization 2
•  Not organizing the entities for structured or faceted search and
retrieval
– ...
HuNI: vernacular classification
•  The user-contributed collections and links give meaning to the data
•  Multiple interpr...
Dr Toby Burrows
Marie Curie Fellow
Department of Digital Humanities
King’s College London
26-29 Drury Lane
London WC2B 5RL...
Alternative approaches
•  Search – use ontologies to classify search results (facets)
•  Topic modeling – automatic genera...
Massive	
  A)ack	
  Tags	
  (last.fm)	
  
00s	
   	
   	
  80s	
   	
   	
  90s	
   	
  acid	
  jazz	
   	
  	
  
alterna1...
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)
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Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)

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Toby Burrows (University of Western Australia and King’s College London) "Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)"
Presentation at the KnoweScape workshop "Evolution and variation of classification systems" March 4-5, 2015 Amsterdam

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Toby Burrows: Vernacular Classification: Knowledge Organization in the Humanities Networked Infrastructure (HuNI)

  1. 1. VERNACULAR CLASSIFICATION: HUMANITIES NETWORKED INFRASTRUCTURE (HUNI) Department of Digital HumanitiesToby Burrows
  2. 2. HuNI (Humanities Networked Infrastructure) •  Aggregates data from 30 different Australian humanities datasets •  Data are defined as entities occurring in the source datasets: 740,000 entities in all •  Harvested records are mapped to one of six basic categories •  No imported relationships between entities •  No de-duplication of entities
  3. 3. Challenges for HuNI •  How to organize and link heterogeneous data for browsing – without entirely pre-determining the structure and relationships •  How to make the aggregated data useful – without imposing too much of a conceptual framework •  How to respect the different disciplinary perspectives reflected in the source datasets •  Researchers need to be able to record and share their views about the data
  4. 4. Concept HuNI Record Category Event Organisation Person Place Work More icons = mo PERSON A natural person ORGANISATION A company, club, trust, gallery, political party, etc WORK A cultural artefact or “man-made” thing created by someone, that has some existence in its own right, either physical or digital PLACE A real, spatial location EVENT An activity that occurs in space and time and may involve people, organisations, places, works, etc. CONCEPT Something whose existence is primarily mental http://wiki.huni.net.au/display/DS/Data+Model
  5. 5. HuNI: creating collections •  Users are able to create their own collections of data •  They can create categories and classifications, and assign individual entities to them •  Users can choose whether to make these collections public •  The list of public collections can be seen and browsed •  Individual entities show which public collections they belong to •  The graph for each entity also shows its membership of a public collection
  6. 6. HuNI: socially-linked data •  Users are also able to create links between entities •  These links are public, by default •  There are no pre-determined links between entities •  Users can add to each others’ links, including disagreeing with them or contradicting them •  Links can describe any kind of reciprocal relationship •  There is no pre-determined ontology or vocabulary of relationships
  7. 7. HuNI: classification and categorization 1 •  Specific individual entities and phenomena are the focus of the HuNI data aggregate •  There is as little pre-defined classification and categorization as possible •  HuNI avoids hierarchical ontological structures (= “flat ontologies”?) •  Entities are organized and presented primarily so that researchers can work with them and manipulate them – classifying entities into collections and creating links between individual entities •  HuNI is not organizing and presenting the entities so as to reflect an authoritative classification or organization of knowledge
  8. 8. HuNI: classification and categorization 2 •  Not organizing the entities for structured or faceted search and retrieval –  Only indexing them for a basic keyword search •  Not organizing them into browsable semantic hierarchies –  Providing only basic browsing via the six categories (and the list of source datasets) •  HuNI is trying to find a middle ground between: –  The linguistic and conceptual limitations of “search” –  The imposition of a single “normative” ontology or classificatory semantic structure
  9. 9. HuNI: vernacular classification •  The user-contributed collections and links give meaning to the data •  Multiple interpretations and perceptions of relationships between entities are encouraged – even if these are contradictory •  Users can express the relationships they see in the data – including classifications and categorizations •  HuNI resists a single normative or expert interpretation or classification of the data •  HuNI encourages the sharing of different perspectives by researchers and other users
  10. 10. Dr Toby Burrows Marie Curie Fellow Department of Digital Humanities King’s College London 26-29 Drury Lane London WC2B 5RL toby.burrows@kcl.ac.uk @tobyburrows tobyburrows.wordpress.com
  11. 11. Alternative approaches •  Search – use ontologies to classify search results (facets) •  Topic modeling – automatic generation of semantic categories and relations from text-based Natural Language Processing •  Linked Data with light categorization for reasoning –  Vocabularies & thesauri encoded for the Semantic Web (SKOS) •  Social tagging or “folksonomies” v  Tags are applied to entities v  There is no formal classification or categorization of concepts v  There are no relationships between tags (other than being used to tag the same entity) v  Research into deriving ontologies from social tagging
  12. 12. Massive  A)ack  Tags  (last.fm)   00s      80s      90s    acid  jazz       alterna1ve    alterna1ve  dance    alterna1ve  rock     ambient    atmospheric    beau1ful       bristol    bristol  sound    bri1sh       chill      chill  out    chillout       dance      dark    downbeat    downtempo    dub       easy  listening    electro    electronic    electronica       england    english    experimental     favorite    favorites    favourite  female  vocalists       hip  hop    hip-­‐hop    house    hypno1c     idm    indie    indie  rock    industrial    instrumental   jazz    lounge    male  vocalists    massive  a@ack     mellow  pop      psychedelic    rap    relax    rock    sexy    soul   soundtrack    technotrance    trip  hop    trip-­‐hop    triphopuk    

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