Some observations on mind map and ontology building tools for knowledge management
Bibliographic record
Abstract
Ontology is a fundamental data object for organizing knowledge in a structured way in many areas ranging from philosophy to Knowledge Management. Knowledge capture, knowledge integration and knowledge delivery are the essential parts of dynamic knowledge management. E-Learning is considered to be an integral part of knowledge delivery system. Information architect plays an important role in developing the system, and are primarily responsible for capturing and modeling knowledge from various Information sources as a part of eLearning. Ontology is found to be useful and efficient as a basis to capture the knowledge, model it in a structured way and disseminate it for further processing from various information sources. In this paper, we present a brief description on the role of ontology in e-learning and review the ontology building tools. The purpose for reviewing ontology building tools is to determine the toolkit most suitable for ontology creation, editing, and mind/concept mapping from the view points of Information Architects (IAs) who play a significant role in designing knowledge management systems. The paper also gives a fundamental understanding of ontology tools available on the market as open source products as well as commercial products in terms of their capability, availability, enhancement and further development. We provide a ranked list of the tools based on our needs and suitability for the IAs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".