A retail ontology: Formal semantics and efficient implementation
Bibliographic record
Abstract
In today's competitive business environment, the ability to make effective decisions is of central importance to an organization's survival. In order to be successful, a modern enterprise should take advantage of all available data. Business intelligence systems can help provide the means to transform the available data into information and derive specific and timely knowledge about the domain. The focus of this research is on building a customer-centric business intelligence system applied to retail. This work describes a retail ontology that can automatically deduce answers to retail queries based upon the system's general knowledge of online retailing and actual data. To be applicable in real world situations, the system should be able to deal efficiently with the huge amounts of data present in retail environments. To this end, this work also introduces a technique for reasoning efficiently with large datasets using state-of-the-art theorem provers or reasoners and existing database technology.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".