Social Semantic Approach to Support Communication in AEC
Bibliographic record
Abstract
Communication systems in the architecture, engineering, and construction (AEC) industry face many challenges. This paper proposes construction information and knowledge portal (CIKP), an information and knowledge-management system that utilizes three technologies to address the challenges in information exchange and knowledge sharing. First, a semantic web that is unlike typical data-exchange standards because ontologies present human knowledge in a machine-interpretable manner. This provides for more linguistic-friendly representation of tacit/subjective knowledge, which increases the level of human-friendliness of communication systems. Second, a social web, which links people (instead of documents) to create communities of practice (CoPs) and allows people to share, reconfigure, and generate knowledge. Finally, publish/subscribe (pub/sub) systems, which provide for a push-pull scenario for information exchange. In the proposed system, any knowledge item (KI) (e.g., a document, website, and blog) will be represented (tagged) with a semantic vector that describes its contents. The developer of the KI can push (share) this to his or her social network. On the other end of the spectrum, system users can build semantic profiles for their areas of expertise and/or interests. The system can pull (find) the most relevant KIs and forward them to the user. The system can also link peers with similar or complementary interest to one another to establish virtual ad hoc teams. The system was evaluated through input from two focus groups.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".