Data Integration and Knowledge Graph Visualization for the Dispersion of Financial Data: A Case Study in Taiwan
Bibliographic record
Abstract
Financial data is crucial for many stakeholders as it provides insights into business operations. The eXtensible Business Reporting Language (XBRL) has become a global standard for data reporting. Despite these standards, issues such as data dispersion remain prevalent; stakeholders often spend considerable time integrating information from various tables and financial reports. To address these challenges, we propose two main research directions: a financial data integration module and a knowledge graph visualization platform. The former automates the processes of data collection, cleaning, and consolidation, while the latter enables stakeholders to more easily view and interpret financial information through knowledge graphs. This study focuses on financial data in Taiwan, illustrating how to integrate dispersed data and enhance visualization with knowledge graphs. Our approach provides a convenient and practical platform that is expected to save significant time and resources, thus maximizing efficiency and value. The platform is accessible at: http://financialdashboard.japaneast.cloudapp.azure.com/en/.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".