Creating Bioinformatics Semantic Web Services from Existing Web Services: A Real-World Application of SAWSDL
Bibliographic record
Abstract
Semantic annotations for WSDL (SAWSDL) is a recently adopted W3C recommendation that provides a mechanism by which WSDL documents can reference external, domain-specific semantic models in order to provide concept-level interoperability of Web Services. Moby is an established protocol for providing semantic Web Services developed by the bioinformatics community: we use Moby to provide a grounding for a SAWSDL implementation in bioinformatics. Our software (Daggoo) allows users to create Moby-compliant semantic Web Services by simply adding SAWSDL markup to existing WSDL files. These new services are compatible with existing Moby services and client software. The Java software we present consists of a proxy servlet, a URI-resolution mechanism, and rule systems for converting back and forth between Moby and XML Schema data formats. As an early implementation of SAWSDL, Daggoo reveals shortcomings in the notation, and several additional technologies needed to achieve real-world semantic interoperability of WSDL-based services. Based on our experience, we suggest how to improve the semantic annotation mechanism, and how to reduce the programming burden for individual service providers. Furthermore, we demonstrate the importance of a semantically-enabled registry for services and data types in facilitating scientist-driven, rather than programmer-driven, Web service choreography.
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.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".