Multidimensional Ontologies for Contextual Quality Data Specification and Extraction
Bibliographic record
Abstract
Data quality assessment and data cleaning are context-dependent activities.Starting from this observation, in previous work a context model for the assessment of the quality of a database was proposed.A context takes the form of a possibly virtual database or a data integration system into which the database under assessment is mapped, for additional analysis, processing, and quality data extraction.In this work, we extend contexts with dimensions, and by doing so, multidimensional data quality assessment becomes possible.At the core of multidimensional contexts we introduce ontologies with provably good properties in terms of query answering (QA).We use the ontologies to represent dimension hierarchies, dimensional constraints, dimensional rules, and specifying quality data.Query answering relies on and triggers dimensional navigation, and becomes an important tool for the extraction of quality data.We introduce and investigate an ontological-multidimensional (OMD) data model for which the aforementioned multidimensional ontology is a particular case.The OMD model extends the traditional multidimensional data model, embedding it into a Datalog ± ontology.The ontology allows for the introduction of generalized facttables, called categorical relations, which may be incomplete and associated to categories at arbitrary levels of the dimensions.The dimensional rules in the ontology are represented as Datalog ± rules, and they enable dimensional navigation while propagating data between different dimension levels, for data completion where data is missing.The dimensional constraints are semantic conditions that have to be satisfied and are represented as Datalog ± constraints.It turns out that the ontologies created vii according to the OMD model correspond to weakly-sticky (WS) programs, for which tractability of conjunctive QA is guaranteed.We analyse the representational and computational properties of the OMD model, we investigate QA and optimization for the WS programs which was only partly studied in the literature.viii
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".