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Revision as of 12:35, 24 November 2012 by Paolo (Talk | contribs)


First day

Moritz Wissenbach

  • CS background
  • interests
    • genetic edition
    • TEI/XML
    • alternative text models
  • projects

Jorge Urzua

  • CS background
  • interests
    • medadata representation in form of a graph
    • knowledge representation
  • projects
    • Max-Planck Inst.
    • Europeana
    • OpenMind project

Alex Czmiel

Ronald Haentjens-Dekker Huygens Institute for the History of the Netherlands The panel convenor

Gregor Middell IT and contemp. lit. background Faust project

Paolo Monella Accademia dei Lincei, Rome Orlandi's textual layers prototype

Tara Andrews , UK and US background byzantine stemmatology text collation and text variation

Federico Meschini library information science and IT background digital libraries and open educational resources

21/11/2012, Morning

Discussion on text modelling.

Two main alternatives to XML:

  • range based model (for annotations)
  • variant graph (for collation)

Tara is using (with Collatex) a graph-based model more complex sthan Schmidt's one.

Paolo is interested in a text model that can represent textual layers (graphical, alphabetical, linguistic).

  • Tara: we can stretch Schmidt's graph to represent the different Orlandi's layers. They're still graphs

Gregor: not variant graphs; they're not variants

Gregor introduces the range-based model and LMNL in particular

Alex has made his master's thesis on LMNL

LMNL seems to be good for Orlandi's 'textual layers'

  • practicality: what can we build on top of e.g. a range-based model (from the datastore to the presentation layer)
  • query/search functions on top of a text model
  • variant graph vs. range-based models
  • processing (equivalent to XSLT?), querying (equivalent to XPath/XQuery)
  • variant graph: traversal patterns?
  • interfaces, APIs, JS libraries
  • problem of variation and how it is handled on different (conceptual) layers of a text
  • common model? can we find a generalized model incorporating features from the variant graph and a range-based model
  • bridge the gap!
  • integration scenarios; import of existing data, multiple use cases on top of those (what is the smallest thing that could possibly work? -- how do we get there)