New Research Directions in Knowledge Discovery and Allied Spheres
Bibliographic record
Abstract
The realm of knowledge discovery extends across several allied spheres today. It encompasses database management areas such as data warehousing and schema versioning; information retrieval areas such as Web semantics and topic detection; and core data mining areas, e.g., knowledge based systems, uncertainty management, and time-series mining. This becomes particularly evident in the topics that Ph.D. students choose for their dissertation. As the grass roots of research, Ph.D. dissertations point out new avenues of research, and provide fresh viewpoints on combinations of known fields. In this article we overview some recently proposed developments in the domain of knowledge discovery and its related spheres. Our article is based on the topics presented at the doctoral workshop of the ACM Conference on Information and Knowledge Management, CIKM 2011.
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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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.018 | 0.060 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".