Towards Semistructured Data Integration
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
With the recent popularity of the World Wide Web, an enormous amount of heterogeneous information is now available online. As a result, information about the same real-world object often spreads over different data sources, and may be partial and inconsistent. How to obtain information as complete as possible and detect inconsistency from these sources is thus a challenge. Previous work using a simple graph-based or tree-based data model to represent heterogeneous data coming from various sites fail to provide a proper foundation for the integration of data with partial and inconsistent information. In order to integrate such data, we need a powerful data model that is more expressive than the existing graph-based and tree-based ones to account for the existence of partial and inconsistent information from different data sources. In this chapter, we propose a novel data model for such data and study how to integrate such data spread in various sources and check consistency in the meantime. We propose a new operator called integration for this purpose and discuss its semantic properties.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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