Beyond Screenshots: Machine-Actionable, Canonical, Semantic Citation of Graphed Data
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Abstract
In 2016 and 2017, a series of conferences for European philologists was organized around the question, “What digital services, collections or curricula need to be developed so that a field of study can flourish in a digital society?” This paper argues for the need to cite graphs of data with machine-actionable canonical citation, independently of the data organized by a graph. It describes ongoing work to implement a “Canonical Graph Service” into the CITE/CTS framework used by the Homer Multitext (HMT). It describes citation of graphs, parts of graphs, and sub-graphs by URN, with some examples of how such URN citations might usefully be resolved. Finally, I discuss the limits of this approach, problems that will not be solved by a Canonical Graph Service. This approach may facilitate the creation of generic tools for documenting syntax across languages, integrating data from diverse projects, and opening new areas of research to scholars outside of quantitative fields.
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