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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)

  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
  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 @tobyburrows
  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
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