Ontological Modeling of Infrastructure Products and Related Concepts
Bibliographic record
Abstract
The large number of infrastructure renewal projects taking place along congested urban transportation corridors poses several challenges for all project proponents. As such, closer integration of processes between all stakeholders is required throughout a project life cycle. This integration can be accomplished through enhancing interorganizational information interoperability. This paper presents ontologies, an emerging tool that is gaining momentum in the computer science field and has great potential to facilitate knowledge sharing and interoperability. A four-layer distributed ontology for representing infrastructure products and related concepts is presented. The root level is a representation of the abstract superclasses (entities and supporting concepts) on which the next levels construct their semantics. The next two levels are considered ontologies specific to the domain of infrastructure products but are created at different levels of detail to maintain consistency with other ontology development efforts. The final level is the application ontology that uses the core knowledge defined at the domain level to create sets of task-specific ontologies. In this paper, an urban infrastructure design coordination ontology is presented. The ontology was then used to build a collaborative system based on the geographic information system to support design coordination of utilities along urban transportation corridors. The system demonstrates how ontologies can be used to streamline the utility design coordination process among utility companies and municipalities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".