Abstract
OCHRE provides a mechanism to create descriptive elements called Properties which are used to identify database items and ascribe qualities to them. This chapter introduces the process referred to as propertizing which is motivated by the goal of classifying items based on their common features and relating items through targeted links, bringing a degree of order to what might otherwise become an unruly collection of unrelated items. OCHRE illustrates how the granularity of an item-based approach, combined with the flexibility of a hierarchical structure, is an effective way to create a user-defined controlled vocabulary, a Taxonomy, that represents the systematic arrangement of descriptive elements used to describe items, and which transcends the restrictions of any single metadata schema. Scholars must be allowed to record meaningful observations about their data in a way that is efficient, flexible, and customizable, without the software placing undue limitations on what may be observed or asked.
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Notes
- 1.
https://www.merriam-webster.com/dictionary/classification; Definition 1.
- 2.
Ibid., Definition 2a.
- 3.
In order to play nicely with other computational systems, OCHRE can transform or export OCHRE data into other popular formats (e.g., CSV, PDF, Word, and Excel), or those needed by client projects (e.g., TEI-XML).
- 4.
D. Schloen, personal communication; from an unpublished conference paper.
- 5.
- 6.
By treating the taxonomy as data composed of items, OCHRE allows for the map** of multiple complex taxonomies onto each other. This approach answers the incorrect assertion that it is not possible to map complex multi-layer typologies onto controlled vocabularies. See the discussion in Lang et al. (2013), where the authors propose the use of SKOS for representing thesauri to map various classificatory typologies.
- 7.
See the OCHRE manual (Schloen and Schloen 2012) for details regarding the different types of variables and their values, Chapter 13, “Organizing Terms in a Taxonomy or Thesaurus,” especially p. 247ff.
- 8.
For a good explanation of the differences between controlled vocabularies, taxonomies, thesauri, and ontologies, see the “Taxonomies & Controlled Vocabularies Special Interest Group of the American Society for Indexing” http://www.taxonomies-sig.org/about.htm.
- 9.
- 10.
While RDF relationships are analogous to our OCHRE properties, their implementation is quite different as they are based on URIs with the intent of modeling linked information for the Semantic Web.
- 11.
- 12.
See, for example, “Figure 5-2: A simplified schematic representation of core and module-specific tables for ARK” (Dufton 2016, p. 376).
- 13.
- 14.
See the OCHRE manual (Schloen and Schloen 2012) for the use of Predefinitions, pp. 72–73. See also below for data entry strategies for faunal data based on Predefinitions.
- 15.
To see how this works itself out for philology, see the RSTI Case Study in Chap. 11.
- 16.
One definition given by Wikipedia (https://en.wikipedia.org/wiki/Skeuomorph) describes the phenomenon of skeuomorphism as “a physical ornament or design on an object made to resemble another material or technique”.
- 17.
Translation of the OCHRE taxonomy into other languages is happening organically, time and energy permitting, often by end-users of OCHRE who have an interest in representing data in their native language. The Google Translation API has been integrated into OCHRE, providing built-in, automated help with translation.
- 18.
- 19.
On the botanical front, by comparison, some OCHRE projects have chosen to be explicit about Family, Genus, and Species, re-using this trio of properties (hierarchically, but non-recursively) to flesh out the relevant branches of the tree of life.
- 20.
https://isac.uchicago.edu/sites/oi.uchicago.edu/files/uploads/shared/docs/oip130.xls; see also Lessons in Data Reuse: A Blind Analysis of Faunal Data from Iran for more information about this data set and some examples of its re-use (http://visiblepast.net/see/antiquity/lessons-in-data-reuse-a-blind-analysis-of-faunal-data-from-iran/).
- 21.
In OCHRE, we would not normally be explicit regarding the absence of a feature, tagging only those properties that do apply to an item, and ignoring those that are not relevant. This gives a concise and uncluttered description of an item. We appreciate, however, that this might be different from “we checked but could not tell,” so the use of an explicit property like Indeterminate might be valuable information, not just the null case.
- 22.
Regular expression is a commonly used pattern-matching syntax for matching string text (see https://en.wikipedia.org/wiki/Regular_expression).
- 23.
OpenRefine is a useful tool to rationalize tabular data (see https://openrefine.org/).
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Schloen, S.R., Prosser, M.C. (2023). An Item-Based Approach: Propertize. In: Database Computing for Scholarly Research. Quantitative Methods in the Humanities and Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-46696-0_5
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