Using entity identification and classification for automated integration of spatial-temporal data
Bibliographic record
Abstract
Big data, crucial to answering economic, social, and political questions facing our society, tend to be diverse and distributed through various sites across the Internet. The creation of tools to integrate and analyze such data is of paramount interest. Yet the automation of these processes continues to be a great challenge. Our work rests on the observation that a great number of public data sources in domains ranging from economic to demographic, although of complex structure, often share key similarities, namely the presence of the Time and Location. Our proposed Data Integration through Object Modeling framework or DIOM tackles the critical problem of automating data integration from a variety of public websites by abstracting key features of multi-dimensional tables and interpreting them in the context of knowledge-centered Unified Spatial Temporal Model. Our classification-driven extractors are trained to identify and classify entities from both structured and unstructured parts of spreadsheets. The unstructured part contained in titles, headers, and footers reveals critical information, so-called Implicit Knowledge, crucial to the correct interpretation of data. Our experimental results on real world datasets from heterogeneous public data sources show increased accuracy by 25% compared to state-of-the-art approaches.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 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".