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.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".