Discovering the Semantics of Relational Tables Through Mappings
Bibliographic record
Abstract
Abstract. Many problems in Information and Data Management require a semantic account of a database schema. At its best, such an account consists of formulas expressing the relationship (“mapping”) between the schema and a formal conceptual model or ontology (CM) of the domain. In this paper we describe the underlying principles, algorithms, and a prototype tool that finds such semantic mappings from relational tables to ontologies, when given as input simple correspondences from columns of the tables to datatype properties of classes in an ontology. Although the algorithm presented is necessarily heuristic, we offer formal results showing that the answers returned by the tool are “correct ” for relational schemas designed according to standard Entity-Relationship techniques. To evaluate its usefulness and effectiveness, we have applied the tool to a number of public domain schemas and ontologies. Our experience shows that significant effort is saved when using it to build semantic mappings from relational tables to ontologies.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".