CONTEXT-AWARE AND ONTOLOGY-DRIVEN KNOWLEDGE SHARING IN P2P COMMUNITIES
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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it