Semantic Interoperability Between Relational Database Systems
Bibliographic record
Abstract
Relational database systems (RDBSs) are well-known and widely used in many organizations, however, semantic conflicts between the participating RDBSs must be resolved before data can be exchanged between them. Semantic resolution between the RDBSs is extremely difficult to address mainly because participating RDBSs are designed and built independently. Furthermore, individual RDBSs are likely to evolve over time and the changes must be reconciled dynamically. In this paper, we describe an approach to resolve the semantic conflicts between RDBSs automatically while allowing the individual RDBSs to evolve. Relational database ontology (RDBO) is created and used to ensure the semantic descriptions of the individual RDBSs are conformed to a set of vocabularies, structures, and restrictions. We show how a modified reasoning engine is used to validate and infer additional semantic relationships from the existing relationships. We also show how terms defined in different database ontologies are compared to each other semantically using semantic weights and our modified reasoning engine. As a result, RDBSs can intemperate with each other seamlessly and at the correct level of semantics defined in their ontologies.
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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 | 0.030 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".