Knowledge Graphs Applications in Smart Cities
Bibliographic record
Abstract
With the invention of advanced technologies, there are millions of options to improve the quality of life in an urban city. Several innovative implementations transform urban cities into smart cities using new technologies to enhance urban inhabitants' efficiency, sustainability, and overall quality of life. Our study shows that knowledge graphs play an important role in smart cities for transportation, parking, traffic, and city development. They serve as significant repositories, bringing together data from various sources. Several crucial domains of smart cities use knowledge graphs to resolve challenges that hinder urban development. In this paper, we discuss the applications of knowledge graphs in various smart city areas, identify existing challenges, and propose strategies to enhance the current implementation of knowledge graphs in smart cities. We highlight a few innovations that used knowledge graphs in smart cities, showcasing their versatility. Integrating knowledge graphs into smart cities significantly enhances the efficiency of urban services by consolidating and connecting data from different sources and constructing a graph, aiding in better decision-making.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".