Digital Design of Paper Technologies Workshop

Transcription

1 Digital Design of Paper Technologies Workshop On the durability and curatibility of Digital Scholarly Editions Daniel Bruder

2 Those who cannot remember the past are condemned to repeat it.

3 Those who cannot reconstruct the archive(s) are doomed to not be able to remember the past?

4 Paper technologies Authors who write with pen and paper often produce non-linear or multi-linear texts. The resulting printed book version ( letzte Hand ) is just the final linearisation step of the author s on-going process of revising the text. To record these revision processes in Digital Scholarly Editions, data structures are needed that can naturally cope with such requirements of multi-linearity. Moreover, the Digital Scholarly Editions and thus the Digital Archives produced in this way must offer two sine qua non qualities, namely: longevity and interoperability.

5 Books stability readability comparability relatibility disseminatibility longevity interoperability

6 Digital Editions search access relation publication transmission collaboration combination mutation...

7 print vs digital? books have a formidable shelf life, they last for centuries digital data doesn t. It is schockingly ephemeral and highly prone to obsolescence we better not falsely rely on the durability of our digital data we better be extremely careful in the curation of our digital assets

8 Digital Scholarly Editions: print vs digital? the practice and methodology of editing is well-established, mature and stable every task that is natural with books needs a custom tool/implementation in the digital medium not everything we can do and offer as programmers is necessarily meaningful and/or useful to humanities scholars needs

9 Digital Humanities meaningful conversation

10 Standards... are a good thing!

11 Standards Motivations behind the TEI restriction to a specific set of tags (schema) to guarantee standardized storage tools to extract relevant parts, according to the schema founded on the OHCO (Ordered Hierarchy of Content Objects) tree model assumption

12 Problems OHCO (Ordered Hierarchy of Content Objects) tree model assumption

13 Problems Natural language (as found in the production stage) of manuscripts and its often non-hierarchical nature Non-hierarchical structures, the tree model assumption and the need for Twisted XML Twisted XML and the fragmentation of knowledge The fragmentation of knowledge and idiosyncracy of the archives The accumulating idiosyncrasy of the archives and the potential non-reconstructibility

14 Twisted XML Lorem ipsum dolor sit amet, consectetur adipisicing elit ` ' comment1

15 Twisted XML Lorem ipsum dolor sit amet, consectetur adipisicing elit ` ' comment1 ` ' comment2

16 Twisted XML Lorem ipsum dolor sit amet, consectetur adipisicing elit `----'`---' comment1 ` ' comment2

17 Twisted XML <p> <span id="comment1" next="comment1-2">lorem</span> <span id="comment2"> <span id="comment1-2">ipsum</span> dolor sit amet </span>, consectetur adipisicing elit </p>

18 Sidenote: Fragmentation of de-facto standards Figure 1: Idiosyncracy

19 Twisted XML These Twisted XML structures are idiosyncratic to each project are beyond what the schema can capture are establishing structures beyond the tree paradigm XML tools are not ready for it Humans are not ready for it dramatically limit the re-use of tools and durability of the data

20 Fully TEI compliant sources still no longevity! Figure 2: Possible Archives?

21 Nested structures? We are essentially dealing with the problem of representing multiple hierarchies.

22 Confident reconstruction? The fragmentation and idiosyncratic nature of these archives make it impossible to re-use and share the much-needed extractors. Any such project needs custom extractors specifically tailored to their data. These custom extractors are expensive. Confidency in these extractors can only be given by the editor (if at all).

23 Meaningful access? There is no protocol for the combination of extractions: what if I want to combine several aspects?

24 A different model we are essentially dealing with graph structures vs. tree structures current practices have an intrinsic obsolescence the archives produced today are essentially dead : difficult to annotate, difficult to disseminate, difficult to reconstruct

25 A different model embracing graphs embracing shared paths over the text, representing different perspectives/readings/hierarchies/aspects precise pre-indexing vs. error-prone post-extraction confident rendering of derivatives: XML/TEI other formats.

26 What if...? What if we could significantly reduce the cognitive load of the transcriber by giving him more natural ways to do his work? we could confidently produce only the intended readings and properly expose the variations and production processes? we could easily add more interesting sources, like diaries, etc and make these truly interoperable? we could effortlessly access each and every single piece of information in the same, generic way? we could flexibly combine these pieces of information? we could progressively go way beyond these isolated pieces of information and their combination and even make knowledge available that is not explicitly recorded? we could produce DSEs in more adequate ways and be more confident on their possible future use?

27 What if.. we could, in general, achieve more accurate results with confidence in higher quality in significantly less time in more adequate and natural ways that are, in fact, re-useable and durable and, ideally, import as much as possible from the old repositories?

28 Graphs vs Trees Figure 3: Facsimile

29 Graphs vs Trees Figure 4: Transcription

30 Graphs Figure 5: Graph structure

31 Graph structures with Edition Operations (a different divide)

32 Edition Operations insertion deletion variation substitution transposition annotation

33 Graph structures d1: i1 = v1: i1 = variated i1: b0 = inserted at the end b0 = This is the baseline with content c1: b0 = < > comment1 c2: b0 = < > comment2

34 Hierarchy and Path Sharing and Specialization Figure 6: Path Sharing

35 Examples and Patterns add: base = dolor base = lorem ipsum sit amet Figure 7: (more on the bonus slides)

36 Benefits By using graph structures we can accurately model, pre-index and record exactly what we intend make post-extraction unnecessary naturally host different potentially overlapping readings, annotations, views and perpectives export to a variety of formats have discernible and intelligible, future-proof sources progressively add and combine with metadata

37 Metadata Figure 8: Pocket Diaries

38 Metadata Figure 9: Prototractratus

39 Full rendering as Linked Open Data for meaningful access By using Semantic Web Technologies and rendering our data into a Linked Open Data Knowledge base, we can meaningfully model and add value to our knowledge do reasoning and inferencing flexibly query and access all aspects integrate with all kinds of external sources and metadata Knowledge Base + Ontology (Vocabulary, Rules) + Reasoners = Inferences Recording of knowledge Production of knowledge, new insights

40 Bonus

41 Deletion Figure 10:

42 Subtitution Figure 11:

43 Variation Figure 12:

44 Transposition Figure 13:

45 Annotation add: base = dolor base = lorem ipsum sit amet anno: base = < > "annotation"