Web service matching for RESTful web services
Bibliographic record
Abstract
There is a growing number of web services available on the Internet, providing a wide range of functionalities. This diversity introduces a variety of new challenges in the field of software engineering - service discovery, integration, and composition, all of which require, to some extent, “service matching”. Web-service matching (or alignment) is the task of mapping the functionalities of two web services, assuming that these functionalities overlap somewhat. In this paper we propose a novel graph-theoretic approach, called Semantic Flow Matching (SFM), for matching REST web services, specified in WADL (Web Application Description Language). The method builds a heterogeneous network of WADL elements and semantically related terms, and uses this network to match similar functionalities of different web services. The method is implemented in a prototype tool that consists of two modules: a converter and a mapper; where the converter wraps the REST web services in WADL format and the mapper module matches web services based on their semantics extracted from the WADL interface build by the converter. We demonstrate the potential of the approach with a small case study.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".