Advances in information and knowledge management
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
Several research areas today overlap between the tracks of databases, information retrieval and knowledge management, such as natural language processing, semantic web, digital libraries, visualization, information quality and data mining. Inter-disciplinary research across these tracks encourages advances in the development of databases, the extraction of information and the discovery of knowledge. This is precisely the focus of our article. We explain the research issues addressed in a Ph.D. workshop recently held at the ACM Conference on Information and Knowledge Management. This workshop had presentations on novel ideas addressing challenges in information and knowledge management. It covered a broad range of topics such as XML architectures, sensor data streams, personal information managers and text pre-processing. In this article, we provide an overview of the research problems and solutions discussed in the Ph.D. workshop. Our article thus describes the latest technological developments in information and knowledge management as seen by academia. This cutting edge technology also finds practical applications in the corporate world.
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.
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 | 0.001 | 0.001 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it