Medieval Manuscripts and Their Migrations: Using SPARQL to Investigate the Research Potential of an Aggregated Knowledge Graph
Bibliographic record
Abstract
Although the RDF query language SPARQL has a reputation for being opaque and difficult for traditional humanists to learn, it holds great potential for opening up vast amounts of Linked Open Data to researchers willing to take on its challenges. This is especially true in the field of premodern manuscripts studies as more and more datasets relating to the study of manuscript culture are made available online. This paper explores the results of a two-year long process of collaborative learning and knowledge transfer between the computer scientists and humanities researchers from the Mapping Manuscript Migrations (MMM) project to learn and apply SPARQL to the MMM dataset. The process developed into a wider investigation of the use of SPARQL to analyse the data, refine research questions, and assess the research potential of the MMM aggregated dataset and its Knowledge Graph. Through an examination of a series of six SPARQL query case studies, this paper will demonstrate how the process of learning and applying SPARQL to query the MMM dataset returned three important and unexpected results: 1) a better understanding of a complex and imperfect dataset in a Linked Open Data environment, 2) a better understanding of how manuscript description and associated data involving the people and institutions involved in the production, reception, and trade of premodern manuscripts needs to be presented to better facilitate computational research, and 3) an awareness of need to further develop data literacy skills among researchers in order to take full advantage of the wealth of unexplored data now available to them in the Semantic Web.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".