Construction and application of knowledge base for hydropower station operation and maintenance based on ontology
Bibliographic record
Abstract
Abstract In the operations management of hydropower stations, there is a problem that a large amount of multi-source heterogeneous structured and unstructured data are challenging to manage and reuse effectively. To improve knowledge organization and collective knowledge sharing, we introduce ontology-based knowledge modeling into the knowledge management and knowledge services of hydropower stations. Specifically, it defines an ontology-based knowledge representation model and constructs a detailed example of ontology knowledge representation and an ontology knowledge base, focusing on three key aspects of hydropower stations, i.e. operation and maintenance of equipment, fault warning and emergency planning. Furthermore, this paper proposes an ontology comprehensive similarity algorithm (OCSA), based on which an ontology-driven visualization application for hydropower knowledge retrieval, prediction and warning, and emergency drill is implemented. Through real-world case studies, the feasibility and effectiveness of the ontology-based knowledge base construction method and critical technology application for hydropower operation and maintenance are demonstrated, improving hydropower stations' knowledge management and application capability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".