What does Software Engineering Practice offer to Semantic Web Service Composition
Bibliographic record
Abstract
Bruce Spencer and Sandy Liu are the leader and a researcher in the Internet Logic group, which specializes in reasoning systems for Internet applications. The group’s activities on the Semantic Web include (i) a Semantic Web Lab with close ties to RuleML.org, (ii) a graduate course at the University of New Brunswick on the Semantic Web Techniques, (iii) BASeWEB (Business Agents and the Semantic Web) workshop held annually in conjunction with the Canadian AI Conference since 2002, (iv) an open source reasoning engine jDREW [2] on SourceForge, (v) a queuing inference engine [3], (vi) DeFleX, an XML Router for agile knowledge workflows [1], and (vii) WSIRD, a rule-based data integration engine between Web Services [4]. Challenges: Web Services can be seen as functional components that can be selected for composition to achieve certain purposes, based on descriptions of the components ’ purposes, their preconditions and effects, and the data they accept and produce. The selection may be done by an abstract reasoner, such as a planner, which uses these descriptions to infer that a specific combination of
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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 | 0.028 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.016 | 0.058 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".