Light-weight semantics and Bayesian Classification: A hybrid technique for dynamic Web Service discovery
Bibliographic record
Abstract
Web Service discovery and ranking has been one of the key issues in Service Oriented Systems. Enormous efforts and research has been done towards semantic modeling of Web Services and a couple of semantic matchmaking and reasoning mechanisms have been developed to allow service consumers search for the required service providers dynamically. These approaches seem to be promising in theory, provided that exhaustive semantic descriptions of the services are available. However, in practice, this is not the case, as current Web Service standards provide quite limited information about services. Therefore, the process of discovery as well as ranking cannot always rely only on the extensive semantic descriptions to be available all the time. However, description of services using light-weight semantics (i.e., non-functional properties) is rather easier to have, and this could be used by classification and machine learning techniques to help in the classification of Web Services at real-time. In this paper, we present a hybrid approach towards enabling dynamic Web service discovery which is based on Bayesian Classification mechanism that classifies different available Web services, representing service providers, based on light-weight semantic descriptions.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".