Developing a project knowledge management framework for tunnel construction: lessons learned in Taiwan
Bibliographic record
Abstract
Knowledge, particularly in the form of experience, can provide a competitive advantage in the construction industry. Knowledge organization (KO) systems developed in the library, information, and computer sciences are excellent for delivering knowledge services. However, it is difficult for these systems to judge how suitable (not just relevant) any of the retrieved knowledge will be in practice. This study puts forward a directional experience ontology (DExOntology) framework, which makes use of KO characteristics to set up problem-topic ontology, but augments the retrieved knowledge documents (KwDocs) in the knowledge bank with additional “orientation” from weighted attributes determined by domain experts, thus bridging the gap between existing KO systems and practical requirements. The goal is a mechanism that delivers knowledge, especially in the domain of practical experience, more efficiently and effectively than simple information retrieval searches. The study finds that the DExOntology framework significantly improves both the relevancy and suitability of the retrieved knowledge, making it a more useful system for construction industry practitioners to use in the field.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".