An ontology-supported asset information integrator system in infrastructure management
Bibliographic record
Abstract
Purpose – The purpose of this paper is to develop and apply an ontology-supported asset information integrator system (AIIS) in the domain of infrastructure management. The two objectives are: first, to describe how different ontologies developed as part of this research support the design of message templates (MTs) that were implemented in the AIIS; and second, to explain the development and application of the prototype system for tangible capital asset (TCA) reporting. Design/methodology/approach – The proposed system was developed in the MS SharePoint platform using a four-step methodology: create a web site and library; review and modify MTs; design and configure workflows; and add functionalities. Findings – First, the architecture, methodology, and evaluation of the two ontologies: Transaction Domain Ontology and Tangible Capital Asset Ontology, developed as part of this research work were briefly introduced to describe how both the ontologies supported the design of MTs that were implemented in the AIIS. Second, the AIIS was successfully developed and applied in the domain of infrastructure management for the Asset Inventory and Condition Assessment Reporting. Practical implications – The development of the AIIS would enable industry experts to exchange the tangible capital information. The built-in search engine and history services would help the experts to search a transaction and track the transaction history. The real-time visualisation of the data would help in decision making. Originality/value – Infrastructure agencies use diversified information systems to manage infrastructure systems. Due to propriety nature of the information systems, the TCA data generated is heterogeneous and inconsistent, which make it difficult to exchange with other organisations. Also, the existing applications focus on processing and managing the TCA data for a variety of tasks; however, lack to support data exchange with other organisations. This emphasises the gap that requires the development of an ontology-supported collaboration system in the domain of infrastructure asset management.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".