Common Sense Reasoning in Automated Database Design
Bibliographic record
Abstract
A great deal of work on automating systems design and development has been carried out, especially in the database area. Systems that semi-automate the database design process have been developed. These systems are interactive in that they may need to ask the user (usually, a database designer) for clarification. The result is that the system asks questions to the user that make the system look less intelligent than it should. This general type of problem has long been recognized with a proposed approach to overcoming it being the incorporation of common sense knowledge into a design system. The View Creation System is an expert system that plays the role of a database designer. With it, a user knowing little about database technology can express his or her database design requirements, which are represented by an entity-relationship model and then translated into a normalized relational model. The system contains a great deal of knowledge about database design, but little, if any, about the user’s application. This forces the user to specify many trivial facts that would be known by any human designer. To overcome this limitation, a Common Sense Business Reasoner is being developed that has a knowledge base containing general knowledge about the world and a reasoning tool to apply this knowledge to a database design task. An empirical study is carried out to simulate and assess the effectiveness of adding the Common Sense Business Reasoner to the View Creation System.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".