Flexible interface matching for Web-service discovery
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 Web-services stack of standards is designed to support the reuse and interoperation of software components on the Web. A critical step, to that end, is service discovery, i.e., the identification of existing Web services that can potentially be used in the context of a new Web application. UDDI, the standard API for publishing Web-services specifications, provides a simple browsing-by-business-category mechanism for developers to review and select published services. In our work, we have developed a flexible service discovery method, for identifying potentially useful services and assessing their relevance to the task at hand. Given a textual description of the desired service, a traditional information-retrieval method is used to identify the most similar service description files, and to order them according to their similarity. Next, given this set of likely candidates and a (potentially partial) specification of the desired service behavior, a structure-matching step further refines and assesses the quality of the candidate service set. In this paper, we describe and experimentally evaluate our Web-service discovery process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it