A Semantic-Based Service Discovery Framework for Collaborative Environments
Bibliographic record
Abstract
In recent years, service-oriented and ubiquitous technologies have experienced impressive development.As these services grow rapidly both in scale and type, effective and accurate service discovery methods play an increasingly important role in the search and selection of services that match consumer requirements and preferences.In order to discover the optimum service and enhance the effectiveness of discovered results, a semantic-based service discovery framework, consisting of user model, context model, service model and a service discovery process, was presented in this study.Then the personalized service ontology was introduced to adjust the service search range adaptively on the basis of the service ontology structure and user information.Furthermore, a semantic-based service discovery method was designed in the proposed framework, which enabled names, attributes and relations of services to be more accurately matched and mapped with user preferences.Finally, to evaluate the effectiveness and accuracy of this method, the simulation analysis was conducted based on service ontology, in which information on 102 separate services and 10 scenarios were extracted from actual data.The simulation results show that compared with the keywords-based method, the proposed semantic-based method shows an increase in recall rate, precision and F-measure.The simulation results also reveal that the proposed method improves service discovery efficiency and performs well in accuracy.Therefore, collaborative environments considered in service discovery can provide useful and effective guidance to study the service recommendation.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".