An evaluation of uplift mapping languages
Bibliographic record
Abstract
Purpose This paper aims to evaluate the state-of-the-art in CSV uplift tools. Based on this evaluation, a method that incorporates data transformations into uplift mapping languages by means of functions is proposed and evaluated. Typically, tools that map non-resource description framework (RDF) data into RDF format rely on the technology native to the source of the data when data transformation is required. Depending on the data format, data manipulation can be performed using underlying technology, such as relational database management system (RDBMS) for relational databases or XPath for XML. For CSV/Tabular data, there is no such underlying technology, and instead, it requires either a transformation of source data into another format or pre/post-processing techniques. Design/methodology/approach To evaluate the state-of-the-art in CSV uplift tools, the authors present a comparison framework and have applied it to such tools. A key feature evaluated in the comparison framework is data transformation functions. They argue that existing approaches for transformation functions are complex – in that a number of steps and tools are required. The proposed method, FunUL, in contrast, defines functions independent of the source data being mapped into RDF, as resources within the mapping itself. Findings The approach was evaluated using two typical real-world use cases. The authors have compared how well our approach and others (that include transformation functions as part of the uplift mapping) could implement an uplift mapping from CSV/Tabular into RDF. This comparison indicates that the authors’ approach performs well for these use cases. Originality/value This paper presents a comparison framework and applies it to the state-of-the-art in CSV uplift tools. Furthermore, the authors describe FunUL, which, unlike other related work, defines functions as resources within the uplift mapping itself, integrating data transformation functions and mapping definitions. This makes the generation of RDF from source data transparent and traceable. Moreover, as functions are defined as resources, these can be reused multiple times within mappings.
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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 | 0.061 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".