CONTEXT-AWARE AND ONTOLOGY-DRIVEN KNOWLEDGE SHARING IN P2P COMMUNITIES
Bibliographic record
Abstract
AbstractThe knowledge management portfolio includes knowledge sharing as a means to connect knowledge to knowledge and knowledge to actors to support decision making, problem solving, viewpoint resolution, conflict negotiation, education and innovation. A knowledge sharing activity comprises two elements—(i) the content of the knowledge being shared and (ii) the context within which the knowledge is being shared. The context in which knowledge is sought and shared amongst peers is of significant importance in establishing the relevance and applicability of the knowledge content. In this paper we present a context-aware, ontology-driven knowledge-sharing framework that leverages ontology to both describe the knowledge sharing actors and the knowledge being shared. We model knowledge sharing in a peer-to-peer (P2P) network. Our P2P knowledge sharing framework comprises: (a) a domain ontology that is used to semantically model each peer; each peer is described as an instantiation of the ontology, (b) a weighted structural graph-based approach to establish affinity between peers and their contexts for the purpose of sharing relevant, needed knowledge resources, and (c) a task-feature relevance matrix to model the domain tasks influencing contextual affinity determination.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".