Web Service Matchmaking Using a Hybrid of Signature and Specification Matching Methods
Bibliographic record
Abstract
Web services are independent software systems designed to offer machine-to-machine interactions over the WWW to achieve well-described operations. Web Service Matchmaking is a process of searching (or discovering) for a web service within a service registry (such as UDDI) to discover a web service that satisfies certain functional requirements as requested by a service consumer. In this paper, we present a hybrid web service matchmaker that analyzes both the signature and specification of a web service. For signature level service matching, we have developed two techniques: (i) logical similarity measures applied to the services' input/output concepts, and (b) non-logical matching based on a Structure Preserving Semantic Matching algorithm. For specification matching, we have introduced a short sentence matching approach applied to a services' description. We evaluated the performance of the matchmaker using the OWLS-TC dataset, and furthermore compared its performance with the similar hybrid matchmaker. Our results indicate a significant improvement in recall and slight improvement in precision.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".