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 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".