A hybrid object clustering strategy for large knowledge-based systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Object bases underlying knowledge based applications tend to be complex and require management. This research aims at improving the performance of object bases underlying a class of large knowledge based systems that utilize object oriented technology to engineer the knowledge base. A hybrid clustering strategy that beneficially combines semantic clustering and iterative graph partitioning techniques has been developed and evaluated for use in knowledge bases storing information in the form of object graphs. It is demonstrated via experimentation that such a technique is useful and feasible in realistic object bases. A semantic specification mechanism similar to placement trees has been developed for specifying the clustering. The workload and the nature of object graphs in knowledge bases differ significantly from those present in conventional object oriented databases. Therefore, the evaluation has been performed by building a new benchmark called the Granularity Benchmark. A segmented storage scheme for the knowledge base using large object storage mechanisms of existing storage managers is also examined.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it