Exploratory Visual Analysis and Interactive Pattern Extraction from Semi-Structured Data
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
Semi-structured documents are a common type of data containing free text in natural language (unstructured data) as well as additional information about the document, or meta-data, typically following a schema or controlled vocabulary (structured data). Simultaneous analysis of unstructured and structured data enables the discovery of hidden relationships that cannot be identified from either of these sources when analyzed independently of each other. In this work, we present a visual text analytics tool for semi-structured documents (ViTA-SSD), that aims to support the user in the exploration and finding of insightful patterns in a visual and interactive manner in a semi-structured collection of documents. It achieves this goal by presenting to the user a set of coordinated visualizations that allows the linking of the metadata with interactively generated clusters of documents in such a way that relevant patterns can be easily spotted. The system contains two novel approaches in its back end: a feature-learning method to learn a compact representation of the corpus and a fast-clustering approach that has been redesigned to allow user supervision. These novel contributions make it possible for the user to interact with a large and dynamic document collection and to perform several text analytical tasks more efficiently. Finally, we present two use cases that illustrate the suitability of the system for in-depth interactive exploration of semi-structured document collections, two user studies, and results of several evaluations of our text-mining components.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 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 it