Towards a Knowledge-Based Framework for Enterprise Content Management
Bibliographic record
Abstract
Nowadays, critical information that is contained in mostly unstructured documents is increasingly becoming a key business resource. Accordingly, enterprises need a foundation for managing content to understand its value and transform it into information and organizational knowledge. Enterprise Content Management (ECM) is an integrated approach to Information Management. There is a need for enhancing this approach to support the transformation from information into organizational knowledge. However, assessing, organizing, sharing, and using content based on knowledge perspectives are crucial, especially for knowledge-intensive enterprises. Those enterprises provide knowledge-intensive products and services that require a robust foundation for knowledge management and innovation capacity. We present the KBCM (Knowledge-Based Content Management) framework for ECM based on the perspective of knowledge components. This paper seeks to create more business value by transforming content into valuable information assets and then from information into organizational knowledge. To demonstrate the framework, an illustrative example is constructed and evaluated.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".