Distributed Ontology Architecture for Knowledge Management in Highway Construction
Bibliographic record
Abstract
The ongoing plethora of rehabilitation in the infrastructure domain requires more planning and integration during design and construction. To achieve this, there is a need for developing and using semantic (ontology-based) mechanisms for the exchange of development knowledge among all project stakeholders. This paper presents a distributed ontology architecture for knowledge management in highway construction. With every other utility tied to the highway geometry, the architecture is intended to be the base for a cross-discipline knowledge exchange in the infrastructure domain. The architecture presents highway knowledge on three levels: domain knowledge (an umbrella for infrastructure shared knowledge), application knowledge (representation of highway-specific knowledge), and user knowledge (an enterprise-specific representation of highway knowledge). The proposed architecture models highway concepts using six major root concepts: project, process, product, actor, resources, and technical topics (attributes and constraints). The architecture was developed using rigorous knowledge acquisition and ontology development techniques. It was developed as an extension for the e-COGNOS ontology. The architecture was validated through input from domain experts.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".