Towards the Semantic Grid: A State of the Art Survey of Semantic Web Services and their Applicability to Collaborative Design, Engineering, and Procurement
Bibliographic record
Abstract
Today, organizations within the engineering and manufacturing domains place as much emphasis on the management and flow of knowledge through a value chain as they do commodities that are more tangible in nature. For example, parts suppliers in the Canadian automotive sector are often asked to collaborate with auto manufacturers in designing and engineering their product, instead of simply producing and supplying it. Such fundamental changes in the overarching economics of this industry have led to a greater focus on collaboration, both in terms of communicating across geographic divides to design components, as well as new requirements to merge heterogeneous data stores in order to manage this distributed procurement process. Our work on this project centred on finding solutions to the above by surveying the state of the industry, as well as assessing the potential employability of related tools in the workplace. It was concluded that the Access Grid (a low-cost, open-source videoconferencing platform) held significant potential to facilitate the high-quality sharing of audiovisual material, while semantic technologies (the “semantic web” and “semantic web services”) represented a feasible solution to the issues of data integration. When combined, these technologies form the “semantic grid”, the focus of this paper. Overall, it is concluded that the past and present business success of this ICT in the information management sector may, with future work, link databases with the visualization interface to provide concurrent cost-benefit analyses.
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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.027 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".