Schema clustering and retrieval for multi-domain pay-as-you-go data integration systems
Bibliographic record
Abstract
A data integration system offers a single interface to multiple structured data sources. Many application contexts (e.g., searching structured data on the web) involve the integration of large numbers of structured data sources. At web scale, it is impractical to use manual or semi-automatic data integration methods, so a pay-as-you-go approach is more appropriate. A pay-as-you-go approach entails using a fully automatic approximate data integration technique to provide an initial data integration system (i.e., an initial mediated schema, and initial mappings from source schemas to the mediated schema), and then refining the system as it gets used. Previous research has investigated automatic approximate data integration techniques, but all existing techniques require the schemas being integrated to belong to the same conceptual domain. At web scale, it is impractical to classify schemas into domains manually or semi-automatically, which limits the applicability of these techniques. In this paper, we present an approach for clustering schemas into domains without any human intervention and based only on the names of attributes in the schemas. Our clustering approach deals with uncertainty in assigning schemas to domains using a probabilistic model. We also propose a query classifier that determines, for a given a keyword query, the most relevant domains to this query. We experimentally demonstrate the effectiveness of our schema clustering and query classification techniques.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".