A Graph-Based Approach to Web Services Composition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automatic composition of Web services has drawn a great deal of attention recently. By composition, we mean taking advantage of currently existing Web services to provide a new service that does not exist on its own. Therefore, in order to have a more complex service, we can use some semantically related simpler Web services and execute them in such a way that the whole set provides the desired service. There are Web service specification languages that specify semantic properties of Web services. These languages are helpful in searching for those Web services that can participate in a composition. This work is aimed at searching among Web services in order to find those whose composition provides a specific behavior. Those Web services found after this search are incrementally composed together to build a new service that realizes that behavior. Our technique takes advantage of graph structures and also a particular formalism called interface automata.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it