Using Ontologies to Overcoming Drawbacks of Databases and Vice Versa: A Survey
Bibliographic record
Abstract
For a same domain, several databases (DBs) exist.The emergence of classical web to the semantic web has contributed to the appearance of the notion of ontology that have shared and consensual vocabulary.For a given, it is more interesting to take advantage of existing databases, to build an ontology.Most of the data are already stored in these databases.So many DBs can be integrated to enable reuse of existing data for the semantic web.Even for existing ontologies, the relevance of the information they contain requires regular updating.These databases can be useful sources to enrich these ontologies.In the other hand, for these ontologies more than the ratio 'size of the instances on the size of working memory' is large more than the management of these instances, in memory, is difficult.Finding a way to store these instances in a structured manner to satisfy the needs of performance and reliability required for many applications becomes an obligation.As a consequence, defining query languages to support these structures becomes a challenge for SW community.We will show through this paper how ontologies can benefit from DBs to increase system performance and facilitate their design cycle.The DBs in their turn suffers from several drawbacks namely complexity of the design cycle and lack of semantics.Since ontologies are rich in semantic, DBs can profit from this advantage to overcoming their drawbacks.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.024 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".