A Semantic Approach to Discovering Schema Mapping Expressions
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Bibliographic record
Abstract
In many applications it is important to find a meaningful relationship between the schemas of a source and target database. This relationship is expressed in terms of declarative logical expressions called schema mappings. The more successful previous solutions have relied on inputs such as simple element correspondences between schemas in addition to local schema constraints such as keys and referential integrity. In this paper, we investigate the use of an alternate source of information about schemas, namely the presumed presence of semantics for each table, expressed in terms of a conceptual model (CM) associated with it. Our approach first compiles each CM into a graph and represents each table's semantics as a subtree in it. We then develop algorithms for discovering subgraphs that are plausible connections between those concepts/nodes in the CM graph that have attributes participating in element correspondences. A conceptual mapping candidate is now a pair of source and target subgraphs which are semantically similar. At the end, these are converted to expressions at the database level. We offer experimental results demonstrating that, for test cases of non-trivial mapping expressions involving schemas from a number of domains, the "semantic" approach outperforms the traditional technique in terms of recall and especially precision.
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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 | 0.001 |
| Open science | 0.000 | 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