Model transformations to bridge concrete and abstract syntax of web rule languages
Bibliographic record
Abstract
This paper presents a solution to bridging the abstract and concrete syntax of a Web rule languages by using model transformations. Current specifications of Web rule languages such as Semantic Web Rule Language (SWRL) or RuleML define their abstract syntax (e.g., metamodel) and concrete syntax (e.g., XML schema) separately. Although the recent research in the area of Model-Driven Engineering (MDE) demonstrates that such a separation of two types of syntax is a good practice (due to the complexity of languages), one should also have tools that check validity of rules written in a concrete syntax with respect to the abstract syntax of the rule language. In this study, we use the REWERSE I1 Rule Markup Language (R2ML), SWRL, and Object Constraint Language (OCL), whose abstract syntax is defined by using metamodeling, while their textual concrete syntax is defined by using either XML/RDF schema or Extended Backus-Naur Form (EBNF) syntax. We bridge this gap by a bi-directional transformation defined in a model transformation language (ATLAS Transformation Language, ATL). This transformation allowed us to discover a number of issues in both web rule language metamodels and their corresponding concrete syntax, and thus make them fully compatible. This solution also enables for sharing web rules between different web rule languages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".