Clustering WSDL Documents to Bootstrap the Discovery of Web Services
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
The increasing use of the Web for everyday tasks is making Web services an essential part of the Internet customer's daily life. Users query the Internet for a required Web service and get back a set of Web services that may or may not satisfy their request. To get the most relevant Web services that fulfill the user's request, the user has to construct the request using the keywords that best describe the user's objective and match correctly with the Web Service name or location. Clustering Web services based on function similarities would greatly boost the ability of Web services search engines to retrieve the most relevant Web services. This paper proposes a novel technique to mine Web Service Description Language (WSDL) documents and cluster them into functionally similar Web service groups. The application of our approach to real Web services description files has shown good performance for clustering Web services based on function similarity, as a predecessor step to retrieving the relevant Web services for a user request by search engines.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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