Data Integration and Knowledge Graph Visualization for the Dispersion of Financial Data: A Case Study in Taiwan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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/.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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 it